The Context Engineering Handbook: Build reliable, high-performance LLM systems using Scalable RAG Architectures with LlamaIndex and Vector Databases
Struggling to keep your AI agents accurate and scalable in a world of exploding data and token limits?
The Context Engineering Handbook offers a step-by-step blueprint for mastering context engineering-the art of building reliable, high-performance LLM systems using Scalable RAG architectures, LlamaIndex, and modern vector databases. You'll move beyond proof-of-concept prompts into production-grade pipelines that deliver consistent, cost-effective results.
What's inside:
Discover how to architect end-to-end retrieval-augmented generation (RAG) workflows that:
Ingest and chunk documents with precision
Embed and store vectors in Pinecone, Weaviate, or Qdrant
Design dynamic prompt pipelines using LangChain and LlamaIndex
Implement real-time streaming ingestion for live updates
Manage memory with hierarchical summaries and scratchpads
Secure inputs, redact PII, and maintain audit-ready logs
You'll gain:
Practical skills to build scalable RAG architectures that serve thousands of requests per second
Hands-on expertise with LlamaIndex data structures and vector database integrations
Proven strategies for cost-monitoring, KV-cache optimization, and token-budget management
Techniques to isolate context in sub-agents and orchestrate complex workflows with Orkes Conductor or LangGraph
Security and compliance best practices, from input sanitization to immutable audit trails
Advanced methods like reinforcement-learning-driven context selection and self-validation testing
Take the next step: Add The Context Engineering Handbook to your toolkit today-your AI systems will thank you.
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