by Joche Ojeda | Feb 23, 2025 | A.I
I’ve been thinking about this topic for a while and have collected numerous notes and ideas about how to present abstractions that allow large language models (LLMs) to interact with various systems – whether that’s your database, operating system, word documents, or other applications.
Before diving deeper, let’s review some fundamental concepts:
Key Concepts
First, let’s talk about APIs (Application Programming Interface). In simple terms, an API is a way to expose methods, functions, and procedures from your application, independent of the programming language being used.
Next is the REST API concept, which is a method of exposing your API using HTTP verbs. As IT professionals, we hear these terms – HTTP, REST, API – almost daily, but we might not fully grasp their core concepts. Let me explain how they relate to software automation using AI.
HTTP (Hypertext Transfer Protocol) is fundamentally a way for two applications to communicate using text. This is its beauty – text serves as the basic layer of understanding between systems, meaning almost any system or programming language can produce a client or server that can interact via HTTP.
REST (Representational State Transfer) is a methodology for systems to communicate and either change or read the state of another system.
Levels of System Interaction
When implementing LLMs for system automation, we first need to determine our desired level of interaction. Here are several approaches:
- Human-like Interaction: An LLM can interact with your operating system using mouse and keyboard inputs, effectively mimicking human behavior.
- REST API Integration: Your application can communicate using HTTP verbs and the REST protocol.
- SDK Implementation: You can create a software development kit that describes your application’s functionality and expose this to the LLM.
The connection method will vary depending on your chosen technology. For instance:
- Microsoft Semantic Kernel allows you to create plugins that interact with your system through REST API, database, or SDK.
- Microsoft AI extensions require you to decide on your preferred interaction level before implementation.
- The Model Context Protocol is a newer approach that enables application exposure for LLM agents, with Claude from Anthropic being a notable example.
Implementation Considerations
When automating your system, you need to consider:
- Available Integration Options: Not all systems provide an SDK or API, which can limit automation possibilities.
- Interaction Protocol Choice: You’ll need to decide between REST API, HTTP, or Model Context Protocol.
This overview should help you understand the various levels of resolution needed to automate your application. What’s your preferred method for integrating LLMs with your applications? I’d love to hear your thoughts and experiences.
by Joche Ojeda | Jan 3, 2024 | A.I
Navigating the Limitations of Large Language Models: Understanding Outdated Information, Lack of Data Sources, and the Comparative Advantages of Retrieval-Augmented Generation (RAG)
Introduction
In the rapidly evolving field of artificial intelligence, Large Language Models (LLMs) like OpenAI’s GPT series have become central to various applications. However, despite their impressive capabilities, these models exhibit certain undesirable behaviors that can impact their effectiveness. This article delves into two significant limitations of LLMs – outdated information and the absence of data sources – and compares their functionality with Retrieval-Augmented Generation (RAG), highlighting the advantages of RAG over traditional fine-tuning approaches in LLMs.
1. Outdated Information in Large Language Models
A prominent issue with LLMs is their reliance on pre-existing datasets that may not include the most current information. Since these models are trained on data available up to a certain point in time, any developments post-training are not captured in the model’s responses. This limitation is particularly noticeable in fields with rapid advancements like technology, medicine, and current affairs.
2. Lack of Data Source Attribution
LLMs generate responses based on patterns learned from their training data, but they do not provide references or sources for the information they present. This lack of transparency can be problematic in academic, professional, and research settings where source verification is crucial. Users may find it challenging to distinguish between factual information, well-informed guesses, and outright fabrications.
Comparing LLMs with Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) presents a solution to some of the limitations faced by LLMs. RAG combines the generative capabilities of LLMs with the information retrieval aspect, pulling in data from external sources in real-time. This approach allows RAG to access and integrate the most recent information, overcoming the outdated information issue inherent in LLMs.
Why RAG Excels Over Fine-Tuning in LLMs
Fine-tuning involves additional training of a pre-trained model on a specific dataset to tailor it to particular needs or improve its performance in certain areas. While effective, fine-tuning does not address the core issues of outdated information and source attribution.
- Dynamic Information Update: Unlike fine-tuned LLMs, RAG can access the latest information, ensuring responses are more current and relevant.
- Source Attribution: RAG provides the ability to trace back the information to its source, enhancing credibility and reliability.
- Customizability and Flexibility: RAG can be customized to pull information from specific databases or sources, catering to niche requirements more effectively than a broadly fine-tuned LLM.
Conclusion
While Large Language Models have transformed the AI landscape, their limitations, particularly regarding outdated information and lack of data source attribution, pose challenges. Retrieval-Augmented Generation offers a promising alternative, addressing these issues by integrating real-time data retrieval with generative capabilities. As AI continues to advance, the synergy between generative models and information retrieval systems like RAG is likely to become increasingly significant, paving the way for more accurate, reliable, and transparent AI-driven solutions.
