Kyle Davis, the creator of RAGKit, discusses RAG systems covering topics like Knowledge Graph RAG, Function Calling, Tool Selection, Re-ranking, and Quantization. The discussion challenges traditional views on 'Agents' and aims to provide clarity for those working with LLMs / Generative AI.
Defining specialized agents enhances problem-solving efficiency and accuracy through gradual optimization over time.
Creating special tokens for instructions in prompts allows efficient compression, enhancing flexibility and model effectiveness.
Evaluating RAG pipelines involves key metrics like faithfulness, answer relevancy, context precision, and context recall to ensure quality answers.
Deep dives
Evaluation Metrics in RAG Pipeline Development
In the development of RAG pipelines, the evaluation metrics play a crucial role. One key metric is faithfulness, which measures the factual consistency of the answer with the provided context. Another metric, answer relevancy, assesses how relevant an answer is to the initial question by generating similar questions from the answer to measure cosine similarity. Context precision measures the quality of chunks added to context, and context recall evaluates how much of the answer can be attributed to the retrieved context.
Use of Just Tokens for Efficient Prompt Compression
The use of just tokens in prompts offers an efficient way to compress prompts for large language models. By creating special tokens to represent certain instructions or tools, prompts can be condensed, allowing for more flexibility and reducing the need for extensive fine-tuning for specific subsets of tools. This approach enhances prompt compression while maintaining the model's effectiveness in understanding and executing prompt instructions.
Multi-Agent Approach for Specialized Problem Solving
Adopting a multi-agent approach for problem solving involves defining agents specialized in specific tasks. These agents can be tailored to excel in particular domains or tasks, allowing for gradual optimization over time as more data is collected. By creating a network of expert agents and a central routing system to direct queries to specialized agents, this approach enhances the efficiency and accuracy of addressing a diverse range of problems.
Challenges of Evaluating Multi-Hop Query Systems
Evaluating multi-hop query systems presents unique challenges, especially in capturing the effectiveness of exploring additional information beyond the initial question. Balancing succinctness with comprehensive information retrieval proves complex, particularly when deciding how to assess the value of additional sentences that extend beyond the gold answer but contribute useful insights obtained from web searches and multi-hop queries.
The Evolution of Client Design in AI Systems
Enhancing the developer experience in client design by focusing on critical elements like feature sets, performance, documentation, and editor support. Utilizing openapi specs to generate clients in multiple languages efficiently, enabling adherence to language-specific best practices. Exploring the benefits of lower-level languages like Go or Rust for optimizing specific parts of the system, leading to significant speed improvements in certain areas.
Advancements in Prompt Optimization and Self-Discovery in AI Systems
Discussing the potential of prompt optimization tools like DSPY's copro optimizer and self-discovery to enhance prompt generation. Leveraging evolutionary algorithms and few-shot examples in prompt optimization to cater to specific model requirements such as LLMs and varying tasks. Exploring the self-discovery approach for AI tasks to determine the most suitable prompts and meta-prompts needed for optimal performance.
Hey everyone! I am SUPER excited to publish our newest Weaviate podcast with Kyle Davis, the creator of RAGKit! At a high-level, the podcast covers our understanding of RAG systems through 4 key areas: (1) Ingest / ETL, (2) Search, (3) Generate / Agents, and (4) Evaluation. Discussing these lead to all sorts of topics from Knowledge Graph RAG, to Function Calling and Tool Selection, Re-ranking, Quantization, and many more!
This discussion forced me to re-think many of my previously held beliefs about the current RAG stack, particularly the definition of “Agents”. I came in believing that the best way of viewing “Agents” is an abstraction on top of multiple pipelines, such as an “Email Agent”, but Kyle presented the idea of looking at “Agents” as scoping the tools each LLM call is connected to, such as `read_email` or `calculator`. Would love to know what people think about this one, as I think getting a consensus definition of “Agents” can clarify a lot of the current confusion for people building with LLMs / Generative AI.
Get the Snipd podcast app
Unlock the knowledge in podcasts with the podcast player of the future.
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode
Save any moment
Hear something you like? Tap your headphones to save it with AI-generated key takeaways
Share & Export
Send highlights to Twitter, WhatsApp or export them to Notion, Readwise & more
AI-powered podcast player
Listen to all your favourite podcasts with AI-powered features
Discover highlights
Listen to the best highlights from the podcasts you love and dive into the full episode