Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Is your RAG pipeline just a 'chat with your documents' demo? Learn the advanced patterns required to solve real-world enterprise challenges with production-grade accuracy.

#1about 4 minutes

Understanding the basic RAG pipeline and its limitations

The standard retrieval-augmented generation pipeline is reviewed, highlighting common business needs like explainability and accuracy that require more advanced solutions.

#2about 3 minutes

Improving accuracy with advanced search and data preparation

Techniques like hybrid search, post-retrieval reranking, and recursive data summarization with Raptor are used to enhance retrieval accuracy.

#3about 3 minutes

Introducing agentic RAG for complex reasoning tasks

Agentic RAG systems add reasoning capabilities to basic pipelines by incorporating tools, memory, planning, and reflection to work towards a goal.

#4about 2 minutes

Implementing self-critique with the corrective RAG pattern

The corrective RAG pattern improves reliability by adding a grading step to evaluate retrieved documents for relevance before generating a response.

#5about 1 minute

Expanding search with query translation and RAG fusion

RAG fusion rewrites a single user query from multiple perspectives to cast a wider net and improve the chances of finding relevant information.

#6about 1 minute

Enabling actions with tool use and function calling

Providing an LLM with a defined set of tools, such as vector search or a calculator, allows it to perform specific actions beyond text generation.

#7about 3 minutes

Orchestrating tasks with advanced planning techniques

Planning evolves from simple routing to complex, parallel execution using directed acyclic graphs (DAGs) generated by an LLM compiler.

#8about 1 minute

Exploring experimental multi-agent collaboration frameworks

Hierarchical multi-agent systems create a separation of concerns by allowing specialized atomic agents to delegate tasks and collaborate on complex queries.

#9about 2 minutes

Key considerations for deploying RAG systems in production

Successfully deploying RAG requires managing token costs and latency, implementing guardrails, ensuring data quality, and being aware of the unreliability of advanced planning.