Columbus

It has been claimed, that to create the ultimate Retrieval Augmented Generation (RAG) Stack, all one needs is a plain vector database, a touch of LangChain, and a smattering of OpenAI. Here are the 10 essential considerations when delving into constructing a RAG from a business perspective*.

1. Data Access and Life Cycle Management:

• How will you effectively manage the entire lifecycle of the information, from acquisition to deletion. This includes connecting to different enterprise data sources and collecting extensive, varied data swiftly and accurately.
• The next step is ensuring that every piece of data is efficiently processed, enriched, readily available, and eventually archived or deleted when no longer needed. This involves constant monitoring, updates, and maintenance, to meet data integrity, security, business needs and compliance standards.

2. Data Indexing & Hybrid Search

• Data indexing and the operation of hybrid search within a large-scale enterprise context are complex, ongoing endeavors that extend beyond initial setup. It involves creating structured, searchable representations of vast datasets, allowing efficient and precise retrieval of information. A hybrid search amplifies this complexity by combining different search methodologies to deliver more comprehensive and relevant results.
• Maintaining such a large-scale index over time is non-trivial; it necessitates continuous updates and refinement to ensure the accuracy and relevance of the retrieved data, reflecting the most current state of information.

3. Enterprise Security & Access Control at Scale

• Enterprise security, especially respecting complex Access Control Lists (ACL), is a crucial aspect of data management and system interactions in any organization. Proper implementation of ACLs is paramount to ensure that every user gains access only to the resources they are permitted to use, aligning with their role and responsibilities within the organization.
• The task is not just about setting up strict access controls but maintaining and updating them in a dynamically changing index (see previous point) to adapt to evolving organizational structures and roles.
• Any cloud system interaction with an enterprise opens attack vectors. The setup of such a system needs to be well thought through. We’ll deal with that in a separate post (and keep it short by pointing to our ISO27001 certification)

4. Chat User Interface

• Building an adaptable chat interface is relatively straight forward. Integrating it with value-add services / agents is where the real challenge lies.
• Recommendations, next-best-action tasks, (semi) autonomous automation are more difficult to implement and integrate as they require a whole lot more of scaffolding behind the scenes.

5. Comprehensive System Interaction

• Developing a system that integrates interactions with indices, Large Language Models (LLM), and performs entailment checks of answers is a multidimensional challenge.
• Building a comprehensive Information Retrieval (IR) Stack is an intricate endeavor. It demands meticulous consideration of the types and sources of data to be incorporated, aiming to achieve a good understanding of the information involved. By accurately accounting for the diversity and nature of data, the system can significantly enhance the quality and relevance of the generated results, providing more precise and contextualized responses.
• In essence, the initial simplicity masks the underlying complexity and sophistication required to orchestrate coherent interactions among various components effectively.

6. Prompt Engineering

• Creating an effective prompt service to facilitate interaction with a Large Language Model (LLM) requires a nuanced approach. It involves crafting prompts that are concise, clear, and contextually rich, to elicit accurate and relevant responses from the LLM.
• The prompts should be designed considering the model’s capabilities and limitations, focusing on clarity and specificity to avoid ambiguous or generalized queries that might result in imprecise or irrelevant answers.
• Additionally, integrating adaptive mechanisms can help refine prompts based on real-time interactions and feedback, enhancing the quality of the dialogue between the user and the LLM. Balancing specificity with adaptability is key in optimizing the efficacy of prompt services interacting with LLMs.

7. Chain of Reasoning

• The implementation of a chain of reasoning represents a sophisticated progression in the journey of developing intelligent systems. It transcends the rudimentary interaction levels, enabling systems to engage in continuous, meaningful dialogue by logically connecting multiple pieces of information.
• This involves not just processing individual queries but understanding and relating pieces of information in a coherent, logical sequence, allowing the system to provide more nuanced, contextual, and insightful responses. It represents a shift from isolated retrieval and response mechanisms to a more integrated, coherent interaction model, where the system can comprehend, relate, and extrapolate information across multiple interaction points, paving the way for more advanced, context-aware conversational experiences.

8. Enterprise Integration

• Integrating a RAG into an existing enterprise setup is the next step, and often more intricate than anticipated, especially when striving to avoid inducing ‘yet another dashboard’ fatigue once the initial novelty diminishes.
• Such integration is not about just plugging in a new component; it demands comprehensive Software Development Kits (SDKs) and thorough interoperability assessments to ensure seamless interaction within the existing technological ecosystem.
• While APIs can offer pathways for integration, relying solely on them is insufficient. They are part of the solution, not the complete answer, serving as components. Achieving seamless integration is about harmonizing new capabilities with established systems, requiring meticulous planning, execution, and ongoing refinement.

9. Continuous Operation

• The continuous operation of advanced systems demands attention to updates, upgrades, and enhancements to sustain optimal performance and adapt to evolving needs. This ongoing endeavor is not only about maintaining the system but also about refining and advancing it continuously.
• A notable point is that the talents who develop such systems are often not the ones who manage them in the long run. The industry is dynamic, and the risk of skilled developers being recruited away is ever-present.

10. Cost Considerations

• Cost considerations are paramount when scaling technologies like LLMs within a company. Early trials, while revealing, often expose vast amounts of data to LLMs, and when these are scaled, the costs escalate significantly. LLM operations tend to be 10-20x more expensive than classic retrievals. Key is the setup a system with a good balance between both
• Operating sophisticated technologies at scale over time, especially with little prior experience, can lead to painful lessons learned in navigating diverse environments and addressing unforeseen challenges. Furthermore, the financial implications extend beyond operational costs to include maintenance, updates, employee training, and support.

Conclusion

Building a perfect RAG stack is not as simple as mixing a few ingredients. It is a meticulous process, riddled with complexities and steep learning curves. It involves considering aspects from data management to continuous operation, enterprise integration, costs, and beyond.

For readers I put the 10 points into an easy-to-use checklist (pdf download).

* We will delve into a technical discussion on the challenges of building RAG at scale in one of the forthcoming posts.

** We’ve been in this business for very long. Gartner thinks of us as the Visionary in the space. Not convinced about the build or buy case? Here’s a paper. And our SquirroGPT solution brings the points made above to life. Try it for yourself.

Last Sunday, I delved into the often misconceived notion of a seemingly easy recipe for a Retrieval Augmented Generation (RAG) solution. Many seem to believe it’s as straightforward as combining a vector database, a sprinkle of LangChain, and a dash of OpenAI. But the reality is far more intricate. Today, we shall investigate a more effective approach that goes beyond these oversimplified ideas.

To illustrate this, let’s revisit the example I mentioned about the bank. This takes us back to the turn of the century when a renowned bank enthusiastically jumped on the Internet bandwagon and started building their own Content Management System, imagining it would be a transformative asset.

However, it soon dawned on them that multi-language content rendering isn’t really a unique selling proposition (USP) of their bank, nor actually of any bank. In the dynamic world of banking and finance, being able to render content in multiple languages is undoubtedly valuable, but it’s hardly a standout feature. And with its own intricacies it isn’t easy either. A multi-million investment, they stopped the ill-fated venture a couple of years later.

What’s the Alternative?

Instead of fixating on what every other organization might be doing or adopting, the mantra should be to channel energy into one’s unique strengths. The answer lies in a two-pronged strategy:

• Leverage External Expertise: Partner with external professionals and vendors who specialize in the core platform. This should be their domain of expertise. Instead of reinventing the wheel, it makes business sense to let the these folks handle what they’re best at – they are hopefully doing it for a long time. Not sure if they are experts? Organizations like Gartner and Forrester are a good starting point. With respect to the RAG dichotomy I suggest to consult e.g. the Gartner Magic Quadrant for Insights Engines. The vendors mentioned know what information retrieval is – better than most upstarts. We’ve been at this for a long time*.
• Hone in on Internal Specialties: Focus internal resources on what isn’t easily available in the market. For our aforementioned bank, this meant developing a cutting-edge portal that provided users with a 15-minute delayed feed of stock market prices and quotes. This might seem commonplace now, but at the time, around 2000, it was groundbreaking. Here’s the kicker: This was a service no content management vendor offered. Yet, the bank possessed the necessary expertise. They understood the user requirements for such a service, had access to the crucial data, and went beyond to make this vision a reality. Notably, this portal still stands today, testament to its utility and the bank’s foresight.

Modern-Day Application

Drawing a parallel to the present day, the principle remains unaltered. Businesses should consider the offerings of platform vendors* and should focus their actual efforts on what sets them apart. A modern-day setup could for instance be:

• Imagine a company selling transportation tickets. Many customer requests for a simple ticket from A to B are already handled well. Their online purchasing process is reasonably straight forward and easy. But here and there a customer needs a more detailed answer or simply wants a trip recommendation. Obvious that a GPT based solution might come in handy.
• Let a vendor like us handle the platform / GPT side of things. Ingestion of data, handling of the full data life cycle process, user authentication and authorization, the actual user interface, and more.
• The company should focus its efforts to “GPT enable” the ticket vending process. This is not something any vendor will provide. For starters because none has access to said system.
• The result: Users can interact with the chatbot to quickly find events, compare ticket prices, and get recommendations. It can answer complex queries, handle multiple event searches, and even assist in transaction processes. This not only improves user experience but also streamlines ticket purchases. Additionally, utilizing the AI’s capabilities, it can predict user preferences based on past interactions, ensuring personalized event suggestions, making ticket buying more intuitive and user-centric. And yes, the same recipe may be applied to any business.

Concluding Thoughts

The lure of the latest technological innovations is strong, and it’s easy to get swept away by the tide. Yet, while integrating solutions like RAG can be valuable, it’s imperative for businesses to recognize what truly sets them apart. Sometimes, the real differentiator might not be a flashy new AI tool but a service or solution that caters to the unique needs of their clientele.

In essence, while the world rushes towards adopting every new technological innovation, it might be more prudent to take a step back, evaluate what truly matters, and invest in those areas. After all, in the vast sea of technology, it’s the unique solutions that stand out and stand the test of time.

* It’s vital to recognize that it’s more intricate than simply merging some Azure Services with Microsoft-provisioned OpenAI access A glaring challenge that comes to mind is the aspect of data integration and re-integration into the existing enterprise landscape. Why this is important? No company runs exclusively on Microsoft products. Thus, relying solely on their offerings can be limiting, especially when data integration extends beyond their product range.

** We’ve been in this business for very long. Gartner thinks of us as the Visionary in the space. Not convinced about the build or buy case? Here’s a paper. And our SquirroGPT solution brings the points made above to life. Try it for yourself.

*** Wondering which bank it is? Ping me. 😉

In the grand tradition of the tech industry, I currently see a number of folks jumping onto the latest bandwagon without a seatbelt. And a number of riders are about to fall into the trap of believing that the latest and greatest tools are the perfect fit for their enterprise needs.

Some are now claiming that a simple vector database, a sprinkle of LangChain, and a dash of OpenAI are the magic recipe for a perfect Retrieval Augmented Generation (RAG) Stack.

Sure, let’s throw in a unicorn and a pot of gold at the end of the rainbow while we’re at it. I’ve seen this movie. Back in the late nineties many self-respecting CIOs and CTOs of Forbes 1’000 companies started to build their own Content Management Systems.

I remember a very large bank to join the bandwagon, too, only to realize that multi-language content rendering is not a bank USP. A decade later we were back at the same game: The eCommerce wave produced a number of large scale fails when retailer CIOs discovered that for a successful web shop you need more than a digital shopping basket.

So here we go again: Just because it’s new and glitzy doesn’t mean it’s golden. A vector database? Sure, it’s a robust system for certain tasks. LangChain? Stellar at simplyfing the cobbling together of such solutions. OpenAI? Undeniably, a giant in text generation. But slap them together and expect enterprise magic? That’s like expecting a random mix of haute couture pieces to make you the next fashion icon. Good luck with that. 

The Misconception of Synergy

Just because individual components excel in their domains doesn’t necessarily mean their combined utility will take you ever beyond 80% of the road. And it’s the last 20% of the road that are the hardest. Outlining just a few of those issues:

• While vector databases excel at retrieving relevant data based on similarities, much of the retrieval has to do with keywords. Not something an out-of-the-box vector database is good at. The way forward will be hybrid search.
• Additionally, the accuracy depends largely on the quality of embeddings and the algorithm’s ability to discern subtle differences. Retrieving a slightly incorrect piece of information can drastically affect the output’s quality. To deal with that you need a lot of experience in classic information retrieval (IR).
• Users might pose queries with multiple valid interpretations. Catering to such ambiguity and ensuring the retrieved knowledge aligns with the user’s intended context can be a significant challenge. You need an entire query parsing and user profiling / scoring setup.
• On top of that most foundational models are trained on vast swaths of internet text, making them susceptible to inherent biases present in the data. Merging retrieved information with such models can sometimes inadvertently perpetuate or even amplify biases.
• In addition, no foundational model will ever understand the finer points of an enterprise’s specific language (e.g. non-public product catalogue). You need a refined IR and eventually graph approach to get to the required precision levels for enterprise use.
• The capability to connect disparate databases can be both a boon and a bane. While it allows for broad knowledge access, ensuring the cohesiveness and consistency of data from different sources can be challenging.
• Which leads to a few other challenges such as data lifecycle, enterprise security, data access control, scaling of such solutions, user interfaces and integration into the existing enterprise landscape, total cost of operation aspects, etc. E.g., as the system evolves and the underlying databases grow, ensuring efficient retrieval without compromising on speed and accuracy becomes challenging.

Conclusion

A generic approach may work and get you 80% of the way. The last 20% to perfection encapsulates the complexities outlined above. It involves iterative refinement, extensive validation, and often, domain-specific adaptations. It’s the nuanced challenges that make the journey to 100% a demanding endeavor.

PS:

We’ve been in this business for very long. Gartner thinks of us as the Visionary in the space. So we got a few insights about build versus buy, shared here. And our SquirroGPT solution brings the points made above to life. Try it for yourself.

In today’s saturated marketplace, there is a cacophony of voices and solutions. Navigating this noise demands differentiation that delivers value. SquirroGPT offers exactly that. Here’s what sets the solution apart:

Three Pillars of Excellence:

At the core of the offering is the concept of Retrieval Augmented LLMs or Retrieval Augmented Generation (RAG) embedded in the solution:

• Evidence-based Results: SquirroGPT is unique in its promise of zero hallucinations. Every piece of information it generates is traceable to a source, ensuring credibility.
• Personalization with Your Own Data: The ability to connect proprietary data sets ensures that the insights you receive are tailored to your business needs. It’s not just an answer; it’s your answer.
• Uncompromising Security: ISO certified, with a fully built out enterprise search stack, SquirroGPT prioritizes the security of enterprise data, which includes fine-grained access level control.

Diving Deeper: The Enterprise Advantage

While many may echo similar sentiments, here’s what sets SquirroGPT apart in the enterprise context:

• Highlighting: Rapidly pinpoint critical data with highlighted passages within the original sources.
• Expand to Enterprise Search: This feature transcends mere text generation, offering powerful search capabilities across enterprise data. Coupled with personalized content recommendations, it transforms how businesses access and utilize information.
• Enterprise-Grade Integration: SquirroGPT’s versatile connectors allow easy integration with existing enterprise tools and platforms.
• Stringent Access Control: Through ACLs, access to data is meticulously managed, reinforcing data security.
• Mastering Data Life Cycle**: It’s not just about using data; it’s about managing it. SquirroGPT champions the complete data life cycle, ensuring that every piece of information is current, accurate, and auditable.
• Seamless Integration with Existing Workbenches: Whether it’s Salesforce, SharePoint, or any other platform, SquirroGPT augments them without the need for overhauls.
• Cost-Effective Excellence: With the innovative Retrieval Augmented LLMs / RAG approach, SquirroGPT optimizes both performance and cost. This means better outcomes without stretching budgets. (As a side note: LLMs are expensive and on most search based operations going solo >10x to 20x more expensive than the RAG approach)
• Graph-Enabled Capabilities: Navigating data becomes an enhanced experience with SquirroGPT’s graph-enabled features, enabling more contextually rich and swift responses.
• Diverse Applications on a Unified Platform: Beyond text generation, SquirroGPT empowers businesses with features like Summarization and Automation, making it a versatile solution for varied challenges.
• Promoting Collaboration: By understanding user profiles and patterns, Squirro streamlines information sharing and discovery, enhancing collaboration across teams.

In summary, SquirroGPT’s unique blend of features, integration capabilities, and cost-effectiveness makes it the go-to choice for enterprises seeking a superior GPT solution. So, if you’re in the market for a solution that punctures the hype balloon with genuine value, talk to us.

Oh and you can try yourself: https://start.squirro.com

The Balance Between Chat Systems and Keyword Search

In the realm of information access and retrieval, the surge in popularity of chat systems, particularly models like ChatGPT, has been nothing short of impressive. These systems, with their ability to understand and generate human-like text, promise a revolution in how we interact with digital platforms. However, amidst this wave of enthusiasm, it’s essential to remember that not all information access needs are best served by chat interfaces. Sometimes, the simplicity and directness of a keyword search can be more effective. Let’s delve into this balance and understand why both systems have their unique place in the digital landscape.

The Rise of Chat Systems and ChatGPT

Chat systems, especially those powered by advanced AI models like ChatGPT, have several compelling advantages:

• Conversational Interaction: Chat systems can understand and respond to user queries in a conversational manner, making the interaction feel more natural and intuitive.
• Contextual Understanding: These systems can grasp the context behind a query, allowing for more nuanced and relevant responses.
• Adaptive Learning: Over time, chat systems can learn from user interactions, refining their responses to better suit individual user preferences and needs.

Given these strengths, it’s no wonder that chat systems are being hailed as the future of digital interaction.

The Enduring Value of Keyword Search

Despite the advancements in chat systems, the traditional keyword search remains a vital tool for information access:

• Directness: Keyword searches offer a direct route to information. If a user knows precisely what they’re looking for, typing in specific keywords can yield results faster than a conversational query.
• Broad Exploration: Keyword searches are excellent for exploring a broad topic. For instance, searching for a term like “solar energy” can provide a wide range of resources, from scientific articles to news reports, allowing users to get a comprehensive view of the topic.
• Simplicity: There’s a straightforwardness to keyword searches that many users appreciate. No need for full sentences or contextual explanations – just type in the key terms and go.
• Predictability: Keyword searches often come with predictable patterns in their results, making it easier for users to sift through and find what they’re looking for.

Balancing Chat and Keyword Search in Information Access

Given the strengths of both systems, it’s clear that a one-size-fits-all approach might not be the best strategy. Instead, platforms can benefit from offering both options in a hybrid setup:

• User Preference: Some users might prefer the conversational approach of chat systems, while others might lean towards the directness of keyword searches. Offering both ensures that user preferences are catered to.
• Query Complexity: For complex queries where the user might not know the exact keywords or is looking for a detailed explanation, chat systems can be invaluable. On the other hand, for straightforward information retrieval, keyword searches might be more efficient.
• Integration Opportunities: There’s potential in integrating both systems. For instance, a user could start with a keyword search and then switch to a chat interaction for further clarification or detailed exploration of a topic.

Making Informed Choices

While it’s easy to get swept up in the excitement surrounding new technologies, it’s crucial for businesses and platforms to make informed choices:

• User Behavior Analysis: Analyze user behavior. Are users primarily looking for quick answers, or are they engaging in more extended, exploratory searches?
• Cost Considerations: Implementing and maintaining advanced chat systems can be resource-intensive. It’s essential to weigh these costs against the potential benefits and consider whether a hybrid approach might be more cost-effective.
• Feedback Loops: Whichever system(s) you implement, ensure that there’s a mechanism for user feedback. This feedback can provide insights into system performance and areas for improvement.

Conclusion

The landscape of information access is evolving, with chat systems like SquirroGPT offering exciting possibilities for user interaction. However, it’s essential to remember the enduring value of traditional keyword searches. By understanding the strengths and limitations of both, platforms can create a more versatile, user-friendly information access environment. As with most things in the digital realm, balance and adaptability are key.

The world of artificial intelligence is rife with innovations, and one of the most notable recent advancements is the Retrieval Augmented Large Language Model (raLLM). While it’s tempting to simplify raLLM as a mere amalgamation of a vector database, some LangChain code, and an OpenAI API, such a reductionist view misses the broader picture. Let’s delve deeper into the intricacies of raLLM and understand why it’s more than just the sum of its parts.

Understanding the Basics

Before diving into the complexities, it’s essential to grasp the foundational elements:

1. Vector Database: This is a database designed to handle vector data, often used in machine learning and AI for tasks like similarity search. Think of giving each sentence, part of sentence or word a vector. The result is a multi-vectorial space It’s crucial for storing embeddings or representations of data in a format that can be quickly and efficiently retrieved.

2. LangChain Code: Without diving too deep into specifics, LangChain code can be seen as a representation of the programming and logic that goes into creating and managing language models and their interactions.

3. OpenAI API (or other LLMs for the matter): This is the interface through which developers can access and interact with OpenAI’s models, including their flagship LLMs ((or other LLMs for the matter)

While each of these components is impressive in its own right, the magic of raLLM lies in how they’re integrated and augmented to create a system that’s greater than its parts.

The Synergy of raLLM

1. Holistic Integration: At a glance, raLLM might seem like a straightforward integration of the above components. However, the true essence of raLLM lies in how these elements are harmonized. It’s not just about connecting a vector database to an LLM via an API; it’s about ensuring that the entire system works in tandem, with each component complementing the others.

2. Advanced Retrieval Mechanisms: While vector databases are efficient at storing and retrieving data, raLLM takes retrieval to the next level. It’s designed to understand context, nuance, and subtleties in user queries, ensuring that the information fetched is not just relevant but also contextually appropriate.

3. Dynamic Interaction: The integration of LangChain code ensures that the raLLM isn’t a static entity. It can dynamically interact with data, update its responses based on new information, and even learn from user interactions to refine its retrieval and response mechanisms.

4. Scalability and Efficiency: One of the standout features of raLLM is its scalability. While traditional LLMs can be computationally intensive, especially when dealing with vast datasets, raLLM is designed to handle large-scale operations without compromising on speed or accuracy. This is achieved through the efficient use of vector databases, optimized code, and the power of LLMs (as you should build this in an LLM agnostic fashion – more of that later in next post).

Beyond Simple Retrieval: The Value Additions of raLLM

1. Contextual Understanding: Unlike traditional search systems that rely solely on keyword matching, raLLM understands context. This means it can differentiate between queries with similar keywords but different intents, ensuring more accurate and relevant results.

2. Adaptive Learning: With the integration of advanced code and LLMs, raLLM has a degree of adaptability. It can learn from user interactions, understand trends, and even anticipate user needs based on historical data.

3. Versatility: raLLM isn’t limited to a specific domain or type of data. Its design allows it to be applied across various industries and use cases, from customer support and content generation to research and data analysis.

Challenges and Considerations

While raLLM offers numerous advantages, it’s also essential to understand its limitations and challenges:

1. Complexity: The integration of multiple components means that setting up and managing raLLM can be complex. It requires expertise in various domains, from database management to AI model training.

2. Cost Implications: Leveraging the power of raLLM, especially at scale, can be resource-intensive. Organizations need to consider the computational costs, especially if they’re dealing with vast datasets or high query volumes. Here raLLM will provide a better cost to value ratio than pure LLM approaches

3. Data Privacy: As with any AI system that interacts with user data, there are concerns about data privacy and security. It’s crucial to ensure that user data is protected and that the system complies with relevant regulations.

Conclusion

The Retrieval Augmented LLM is a testament to the rapid advancements in the AI domain. While it’s built on foundational components like vector databases, LangChain code, and LLMs, its true value lies in the seamless integration of these elements. raLLM offers a dynamic, scalable, and efficient solution for information retrieval, but it’s essential to approach it with a comprehensive understanding of its capabilities and challenges. As the adage goes, “The whole is greater than the sum of its parts,” and raLLM is a shining example of that.

Oh, and you may test a raLLM yourself: Get going with SquirroGPT.

Large Language Models (LLMs) have taken the world of artificial intelligence by storm, showcasing impressive capabilities in text comprehension and generation. However, as with any technology, it’s essential to understand its strengths and limitations. When it comes to search functionality, relying solely on LLMs might not be the best approach. Let’s explore why.

Understanding LLMs: Strengths and Weaknesses

LLMs, like OpenAI’s GPT series, are trained on vast amounts of text data, enabling them to generate human-like text based on patterns they’ve learned. Their prowess lies in understanding context, generating coherent narratives, and even answering questions based on the information they’ve been trained on.

However, one area where LLMs falter is text retrieval. While they can comprehend and generate text, they aren’t inherently designed to search and fetch specific data from vast databases efficiently. This limitation becomes evident when we consider using LLMs for search purposes.

The Challenges of Using LLM for Search

1. Porting the Full Index into LLM: To make an LLM effective for search, one approach would be to port the entire index or database into the model. This means that the LLM would have to be retrained with the specific data from the index, allowing it to generate search results based on that data. However, this process is both time-consuming and expensive. Training an LLM is not a trivial task; it requires vast computational resources and expertise.

2. Exposing the Entire Index at Query Time: An alternative to porting the index into the LLM is to expose the entire index or database at the time of the query. This would mean that every time a search query is made, the LLM would sift through the entire database to generate a response. Not only is this approach inefficient, but it also places immense strain on computational resources, especially when dealing with large databases.

3. High Computational Demands: Both of the above approaches are compute-heavy. LLMs, especially the more advanced versions, require significant GPU infrastructure to operate efficiently. When used for search, these demands multiply, leading to increased operational costs. For businesses or platforms that experience high search volumes, this could translate to unsustainable expenses.

A More Balanced Approach: The Case for raLLM

Given the challenges associated with using LLMs for search, it’s clear that a more nuanced approach is needed. This is where Retrieval Augmented LLMs (raLLM) come into play.

raLLM combines the strengths of LLMs with those of traditional information retrieval systems. While the LLM component ensures coherent and contextually relevant text generation, the information retrieval system efficiently fetches specific data from vast databases.

By integrating these two technologies, raLLM offers a solution that is both efficient and effective. Search queries are processed using the information retrieval system, ensuring speed and accuracy, while the LLM component can be used to provide detailed explanations or context around the search results when necessary.

This hybrid approach addresses the limitations of using LLMs for search. It reduces the computational demands by leveraging the strengths of both technologies where they are most effective. Moreover, it eliminates the need to port the entire index into the LLM or expose it at query time, ensuring a more streamlined and cost-effective search process.

Conclusion

While Large Language Models have revolutionized many aspects of artificial intelligence, it’s crucial to recognize their limitations. Using LLMs for search, given their current design and capabilities, presents challenges that can lead to inefficiencies and increased operational costs.

However, the evolution of AI is marked by continuous innovation and adaptation. The development of solutions like raLLM showcases the industry’s commitment to addressing challenges and optimizing performance. By combining the strengths of LLMs with traditional information retrieval systems, we can harness the power of AI for search in a more balanced and efficient manner.

Oh, and you may test a raLLM yourself: Get going with SquirroGPT.

In the ever-evolving landscape of artificial intelligence, the emergence of Retrieval Augmented LLMs (raLLM) has marked a significant turning point. This innovative approach, which combines an information retrieval stack with large language models (LLM), has rapidly become the dominant design in the AI industry. But what is it about raLLMs that makes them so special? And why are they particularly suited for enterprise contexts? Let’s delve into these questions.

The Fusion of Information Retrieval and LLM

At its core, raLLM is a marriage of two powerful technologies: information retrieval systems and large language models. Information retrieval systems are designed to search and fetch relevant data from indices based on vast databases of data, while LLMs are trained to generate human-like text based on the patterns they’ve learned from massive amounts of data.

By combining these two, raLLMs can not only generate coherent and contextually relevant responses but also pull specific, accurate information from a database when required. This dual capability ensures that the output is both informed and articulate, making it a potent tool for a variety of applications.

The Rise of raLLM as a Dominant Design

We have started to work on raLLM back in early 2022. And would not have foreseen what happened next. Sure, the AI industry is no stranger to rapid shifts in dominant designs. However, the speed at which raLLM has become the preferred choice is noteworthy. Within a short span, it has outpaced other models and designs, primarily due to its efficiency and versatility.

The dominance of raLLM can be attributed to its ability to provide the best of both worlds. While LLMs are exceptional at generating text, they can sometimes lack specificity or accuracy, especially when detailed or niche information is required. On the other hand, information retrieval systems can fetch exact data but can’t weave it into a coherent narrative. raLLM bridges this gap, ensuring that the generated content is both precise and fluent.

raLLM in the Enterprise Context

For enterprises, the potential applications of AI are vast, ranging from customer support to data analysis, content generation, and more. However, the key to successful AI integration in an enterprise context lies in its utility and accuracy.

This is where raLLM shines. By leveraging the strengths of both LLMs and information retrieval systems, raLLM offers a solution that is tailor-made for enterprise needs. Whether it’s generating detailed reports, answering customer queries with specific data points, or creating content that’s both informative and engaging, raLLM can handle it all.

Moreover, in an enterprise setting, where the stakes are high, the accuracy and reliability of information are paramount. raLLM’s ability to pull accurate data and present it in a coherent manner ensures that businesses can trust the output, making it an invaluable tool in decision-making processes.

In conclusion, the emergence of Retrieval Augmented LLMs (raLLM) represents a significant leap forward in the AI industry. By seamlessly integrating the capabilities of information retrieval systems with the fluency of LLMs, raLLM offers a solution that is both powerful and versatile. Its rapid rise to dominance is a testament to its efficacy, and its particular suitability for enterprise contexts makes it a game-changer for businesses looking to harness the power of AI. As we move forward, it’s clear that raLLM will play a pivotal role in shaping the future of enterprise AI.

Oh, and you may test a raLLM yourself: Get going with SquirroGPT.

The other day we were asked to take part in a accelerator program. As always some form to be filled out. I was short on time. In fact it was aleady past the official deadline. But the organizer wanted us absolutely in. So what did I do? I turned to ChatGPT to help me formulate the answers to the questionaire.

And now something scary happened.

One of the questions was about how our startup and product fit the challenge (see next screenshot)

I simply copied the questions into ChatGPT without any additional context. Here’s the answer I got.

So in fact without me providing the specific context of Squirro ChatGPT returned to me the description of another company’s answer to the same questions. I cross-checked this and now it gets scary: Above mentioned company submitted to that same challenge…

So ChatGPT reproduced an answer from somebody else – efficient caching, everything morphing into the same thing (ChatGPT producing the same answer regardless of who asks), the system knows who asked what, when…

PS: After a bit of prompt engineering ChatGPT produced a fairly good answer describing what we do instead what others do and have submitted to the challenge.

We released GPT for the Enterprise – https://squirro.com/enterprise-generative-ai-and-large-language-models/… Obviously we’re trying it on our own stuff, e.g. our ISO Rulebook. And as obvious we try to see if we can break it. Here’s a test. We failed… You can try it yourself: https://start.squirro.com