Advanced Knowledge & Agentic Systems with LLMs

TRAINING DESCRIPTION

This training is the next step after Buzz-Free Programming with LLMs. It moves beyond basic retrieval into the techniques that make LLM-based knowledge systems work in production: advanced and modular RAG, metadata-driven knowledge bases, graph-based retrieval, and self-maintaining knowledge layers. The second half focuses heavily on agents — frameworks, patterns, workflows, and deep agents that solve complex domain problems. As in the previous edition, the approach stays pragmatic and buzz-free: knowing when a technique earns its complexity, and when it doesn’t.

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BASIC PROGRAM

  • Module 1: Advanced and Modular RAG
  • Module 2: Metadata and Knowledge Base Design
  • Module 3: Agentic RAG
  • Module 4: Graph RAG
  • Module 5: Compounding Knowledge: LLM-Wiki and Curation
  • Module 6: Agentic Frameworks and Patterns
  • Module 7: Agentic Workflows and Deep Agents

DETAILED PROGRAM

Module 1: Advanced and Modular RAG

  • Recognizing where naive RAG hits its ceiling in production
  • Pre-retrieval optimization: query rewriting, routing, intent classification, HyDE, step-back prompting
  • Post-retrieval optimization: cross-encoder reranking, context compression, deduplication
  • Context engineering at indexing time rather than query time
  • Modular, composable pipelines: retrievers, rerankers, routers, and validators as swappable components
  • Self-correcting retrieval loops (Self-RAG)
  • Multimodal and visual document retrieval for scientific and technical documents

Module 2: Metadata and Knowledge Base Design

  • Treating metadata as a context layer, not an afterthought
  • LLM-generated metadata: automated extraction of content, technical, and semantic annotations at ingestion
  • Metadata filtering: non-semantic pre-filtering by type, version, and access before vector search
  • Multi-hop and multi-meta filtering for complex queries
  • Provenance and lineage: source authority, versioning, and chain of custody
  • Governance: access control, freshness and quality scoring, multi-source conflicts
  • Structuring knowledge bases from messy organizational data

Module 3: Agentic RAG

  • Adding a reasoning loop around retrieval: deciding when to retrieve, reformulate, or stop
  • Query decomposition and parallel sub-queries
  • Self-validation and iterative refinement of retrieved context
  • Single-agent versus multi-agent retrieval architectures
  • Knowing when agentic retrieval is worth the added latency and cost

Module 4: Graph RAG

  • Moving beyond vectors: relationships, multi-hop, and entity-centric questions
  • Building and querying knowledge graphs alongside vector stores
  • Hybrid graph and vector retrieval
  • Trade-offs of Graph RAG: cost, maintenance, and when it actually pays off

Module 5: Compounding Knowledge: LLM-Wiki and Curation

  • Shifting from query-time retrieval to ingestion-time knowledge building (the LLM-Wiki pattern)
  • Architecture: immutable sources, derived wiki, and the schema file
  • The lint step: detecting contradictions, stale information, and orphan pages
  • Time-awareness: distinguishing true-but-outdated knowledge from current knowledge
  • Knowledge curation as a continuous, model-maintained process
  • Enterprise reality check: scale, concurrency, and the limits of compiled knowledge layers

Module 6: Agentic Frameworks and Patterns

  • Core agentic patterns: tool use, planning, reflection, and multi-agent collaboration
  • The framework landscape and how to choose between options
  • Type-safe, async-first agent design with Pydantic AI
  • Knowing when a framework adds real value and when it is overhead

Module 7: Agentic Workflows and Deep Agents

  • Workflows versus autonomous agents: predefined orchestration versus model-driven control
  • Deep agents: planning loops, file operations, sub-agent delegation, and sandboxed code execution
  • Human-in-the-loop approval in long-running tasks
  • Building with Deep Agents and Pydantic-Deep
  • Solving complex domain problems through decomposition and self-correction

KEY TAKEAWAYS

  • Move beyond naive RAG to advanced, modular, and graph-based retrieval architectures.
  • Design metadata and knowledge bases that make retrieval reliable, governed, and time-aware.
  • Build self-maintaining knowledge layers that compound over time instead of rediscovering knowledge on every query.
  • Master agentic patterns, frameworks, workflows, and deep agents for complex domain problems.