Practical Context Engineering for AI Developers: Optimize token management, memory stores, and RAG p

Practical Context Engineering for AI Developers: Optimize token management, memory stores, and RAG pipelines with LangChain and Semantic Kernel for real-world projects

Are you wrestling with context limits, spiraling token costs, or unreliable AI outputs? "Practical Context Engineering for AI Developers" tackles these challenges head-on, equipping you with the strategies and code you need to build dependable, high-performance AI systems.

This hands-on guide reveals how to architect context pipelines that scale from prototypes to production. You'll harness LangChain and Semantic Kernel to orchestrate prompt templates, memory stores, and retrieval-augmented generation (RAG) workflows-ensuring every API call is grounded, efficient, and cost-effective.

Inside, you'll learn to:

• Optimize token management so your prompts pack maximum relevance within model limits.

• Configure and combine memory stores-from FAISS and Pinecone to Dragonfly and Redis-for instant, session-aware recall.

• Construct both pure-play and agentic RAG pipelines, weaving together semantic and keyword retrieval, reranking, and multi-model orchestration.

• Automate context compression and token budgeting, using on-the-fly summarization and dynamic selection to control latency and expenses.

• Secure and scale your services, from PII detection and encryption to containerized deployment, Kubernetes auto-scaling, and telemetry-driven monitoring.

• Extend your stack with custom retrievers, external API integrations, domain-tuned embedding models, and feedback-driven improvements.

Packed with up to date, this book empowers AI developers to transform scattered documents, chat logs, and live data feeds into reliable, grounded responses. If you're ready to master context engineering, cut costs, and deliver real-world AI solutions, grab your copy of "Practical Context Engineering for AI Developers" today and start building smarter systems at scale.