Webinar: Introduciton to Agents and RAG (Retrieval-Augmented Generation)
Video
Matt Wyman
,
CEO / Co-Founder
February 27, 2025
Retrieval-Augmented Generation (RAG) has become an essential technique for improving AI-driven applications, but integrating it with agent-based architectures unlocks even more potential. In our recent webinar, we explored how agents and retrieval mechanisms work together, the technical challenges involved, and best practices for implementing a scalable solution.
Key Topics Covered
1. The Agentic RAG Workflow
How retrieval, intent classification, and generation interact
Why vector retrieval works well for language-based queries
The standard RAG flow: query → retrieval → reranking → generation
2. Building a Docs & Blog Agent
Setting up a data pipeline for structured document ingestion
Selecting chunking and embedding strategies to improve retrieval quality
Using a reverse question generator to optimize for accuracy and performance
3. Connecting Retrieval to an Agent Network
Designing an agent with memory and retrieval tools
Integrating vector search with context-aware prompts
Using reranking and past interactions to improve responses
4. Lessons Learned from Implementation
Balancing k-values: Higher values improve accuracy but affect performance
Optimizing embeddings: Generic models work, but domain-specific fine-tuning can help
Iterative refinement: Incorporating real-time evaluation into the ingestion loop
Key Takeaways
Building an Agentic RAG system involves integrating retrieval, reranking, and generation into a seamless workflow. Vector search plays a crucial role in retrieving relevant information efficiently, while agents enhance intent understanding and context preservation. Optimizing chunking strategies, embedding models, and retrieval parameters can significantly impact accuracy and performance. Iterative evaluation, including real-time feedback loops, ensures that the system remains stable and effective in production.
Conclusion
Retrieval-Augmented Generation, when combined with agent-driven architectures, enables more dynamic and intelligent AI applications. By designing robust retrieval pipelines and leveraging vector search, developers can build systems that respond with greater accuracy, adapt to new information, and maintain context across interactions. As the field evolves, refining embedding strategies, retrieval heuristics, and agent coordination will be key to scaling these solutions. Whether you're just exploring RAG or actively implementing it, understanding the trade-offs and optimizations will help you build more reliable and efficient AI-driven applications.
Retrieval-Augmented Generation (RAG) has become an essential technique for improving AI-driven applications, but integrating it with agent-based architectures unlocks even more potential. In our recent webinar, we explored how agents and retrieval mechanisms work together, the technical challenges involved, and best practices for implementing a scalable solution.
Key Topics Covered
1. The Agentic RAG Workflow
How retrieval, intent classification, and generation interact
Why vector retrieval works well for language-based queries
The standard RAG flow: query → retrieval → reranking → generation
2. Building a Docs & Blog Agent
Setting up a data pipeline for structured document ingestion
Selecting chunking and embedding strategies to improve retrieval quality
Using a reverse question generator to optimize for accuracy and performance
3. Connecting Retrieval to an Agent Network
Designing an agent with memory and retrieval tools
Integrating vector search with context-aware prompts
Using reranking and past interactions to improve responses
4. Lessons Learned from Implementation
Balancing k-values: Higher values improve accuracy but affect performance
Optimizing embeddings: Generic models work, but domain-specific fine-tuning can help
Iterative refinement: Incorporating real-time evaluation into the ingestion loop
Key Takeaways
Building an Agentic RAG system involves integrating retrieval, reranking, and generation into a seamless workflow. Vector search plays a crucial role in retrieving relevant information efficiently, while agents enhance intent understanding and context preservation. Optimizing chunking strategies, embedding models, and retrieval parameters can significantly impact accuracy and performance. Iterative evaluation, including real-time feedback loops, ensures that the system remains stable and effective in production.
Conclusion
Retrieval-Augmented Generation, when combined with agent-driven architectures, enables more dynamic and intelligent AI applications. By designing robust retrieval pipelines and leveraging vector search, developers can build systems that respond with greater accuracy, adapt to new information, and maintain context across interactions. As the field evolves, refining embedding strategies, retrieval heuristics, and agent coordination will be key to scaling these solutions. Whether you're just exploring RAG or actively implementing it, understanding the trade-offs and optimizations will help you build more reliable and efficient AI-driven applications.
Retrieval-Augmented Generation (RAG) has become an essential technique for improving AI-driven applications, but integrating it with agent-based architectures unlocks even more potential. In our recent webinar, we explored how agents and retrieval mechanisms work together, the technical challenges involved, and best practices for implementing a scalable solution.
Key Topics Covered
1. The Agentic RAG Workflow
How retrieval, intent classification, and generation interact
Why vector retrieval works well for language-based queries
The standard RAG flow: query → retrieval → reranking → generation
2. Building a Docs & Blog Agent
Setting up a data pipeline for structured document ingestion
Selecting chunking and embedding strategies to improve retrieval quality
Using a reverse question generator to optimize for accuracy and performance
3. Connecting Retrieval to an Agent Network
Designing an agent with memory and retrieval tools
Integrating vector search with context-aware prompts
Using reranking and past interactions to improve responses
4. Lessons Learned from Implementation
Balancing k-values: Higher values improve accuracy but affect performance
Optimizing embeddings: Generic models work, but domain-specific fine-tuning can help
Iterative refinement: Incorporating real-time evaluation into the ingestion loop
Key Takeaways
Building an Agentic RAG system involves integrating retrieval, reranking, and generation into a seamless workflow. Vector search plays a crucial role in retrieving relevant information efficiently, while agents enhance intent understanding and context preservation. Optimizing chunking strategies, embedding models, and retrieval parameters can significantly impact accuracy and performance. Iterative evaluation, including real-time feedback loops, ensures that the system remains stable and effective in production.
Conclusion
Retrieval-Augmented Generation, when combined with agent-driven architectures, enables more dynamic and intelligent AI applications. By designing robust retrieval pipelines and leveraging vector search, developers can build systems that respond with greater accuracy, adapt to new information, and maintain context across interactions. As the field evolves, refining embedding strategies, retrieval heuristics, and agent coordination will be key to scaling these solutions. Whether you're just exploring RAG or actively implementing it, understanding the trade-offs and optimizations will help you build more reliable and efficient AI-driven applications.
Retrieval-Augmented Generation (RAG) has become an essential technique for improving AI-driven applications, but integrating it with agent-based architectures unlocks even more potential. In our recent webinar, we explored how agents and retrieval mechanisms work together, the technical challenges involved, and best practices for implementing a scalable solution.
Key Topics Covered
1. The Agentic RAG Workflow
How retrieval, intent classification, and generation interact
Why vector retrieval works well for language-based queries
The standard RAG flow: query → retrieval → reranking → generation
2. Building a Docs & Blog Agent
Setting up a data pipeline for structured document ingestion
Selecting chunking and embedding strategies to improve retrieval quality
Using a reverse question generator to optimize for accuracy and performance
3. Connecting Retrieval to an Agent Network
Designing an agent with memory and retrieval tools
Integrating vector search with context-aware prompts
Using reranking and past interactions to improve responses
4. Lessons Learned from Implementation
Balancing k-values: Higher values improve accuracy but affect performance
Optimizing embeddings: Generic models work, but domain-specific fine-tuning can help
Iterative refinement: Incorporating real-time evaluation into the ingestion loop
Key Takeaways
Building an Agentic RAG system involves integrating retrieval, reranking, and generation into a seamless workflow. Vector search plays a crucial role in retrieving relevant information efficiently, while agents enhance intent understanding and context preservation. Optimizing chunking strategies, embedding models, and retrieval parameters can significantly impact accuracy and performance. Iterative evaluation, including real-time feedback loops, ensures that the system remains stable and effective in production.
Conclusion
Retrieval-Augmented Generation, when combined with agent-driven architectures, enables more dynamic and intelligent AI applications. By designing robust retrieval pipelines and leveraging vector search, developers can build systems that respond with greater accuracy, adapt to new information, and maintain context across interactions. As the field evolves, refining embedding strategies, retrieval heuristics, and agent coordination will be key to scaling these solutions. Whether you're just exploring RAG or actively implementing it, understanding the trade-offs and optimizations will help you build more reliable and efficient AI-driven applications.
