Designing Long-Context AI Systems: Build Faster, Smarter, and More Scalable LLM Applications Using Memory Architectures, Vector Search, and Retrieval

By Newman Chandler

Designing Long-Context AI Systems: Build Faster, Smarter, and More Scalable LLM Applications Using Memory Architectures, Vector Search, and Retrieval Pipelines
Long-context AI is reshaping how modern applications reason, remember, and operate at scale. Yet most teams still face the same obstacles: models that forget halfway through a task, retrieval pipelines that miss critical information, and systems that collapse under the weight of large documents or long conversations. If you've ever wondered how to push beyond these limits and build AI that actually uses all the information you give it, this book gives you the blueprint.

Designing Long-Context AI Systems shows you how to engineer LLM applications that run faster, think deeper, and scale reliably across real-world workloads. Instead of fighting context limits, you'll learn how to combine memory architectures, vector search, retrieval pipelines, and long-context models into a cohesive, production-ready system.
This is a practical guide for AI/ML engineers, software developers, solution architects, and technical leaders determined to build high-performance AI, without the guesswork. Through clear explanations and proven strategies, you'll understand not just what works but why it works, and how to apply it immediately to your own projects.

You'll learn how to:
- Build RAG pipelines that actually improve accuracy, not just add complexity.
- Manage context windows intelligently through chunking, compression, and structured memory.
- Combine long-context models with retrieval and caching for superior performance.
- Process large documents, codebases, conversations, and multimodal data at scale.
- Deploy systems that maintain relevance, freshness, and low latency, even as data grows.
- Evaluate long-context behavior with the right metrics and testing strategies.
- Reduce hallucinations by grounding LLMs in trustworthy, up-to-date knowledge.
- Operate production systems with strong observability, safety, and cost control.

If you've ever asked questions like: How do I prevent the model from losing information in the middle of a long document?When should I use retrieval, and when is a big context window enough?How do I make my AI system remember across sessions?What architectures scale best when documents and user histories get huge?you'll find clear, direct answers here.

Whether you're building enterprise search systems, intelligent agents, research assistants, or code-understanding tools, the methods in this book will help you produce AI that is accurate, efficient, and dependable.

If you're ready to design AI systems that handle real-world complexity with confidence, and outperform anything you've built before, start reading today.

• Format: Paperback
• Language: English
• ISBN: B0G58ZL3V6
• ISBN13: 9798277548042
• Release Date: December 2025
• Publisher: Independently Published
• Length: 240 Pages
• Weight: 0.93 lbs.
• Dimensions: 0.5" x 7.0" x 10.0"