Custom LLM Workflows with LangChain & OpenAI From Prompt Engineering to Production: Practical Python Recipes to Accelerate, Secure, and Optimize AI-Po
Are your AI workflows truly production-ready, or are subtle pitfalls costing you accuracy, efficiency, and user trust?
In Custom LLM Workflows with LangChain & OpenAI from Prompt Engineering to Production, you'll gain hands-on expertise to engineer robust, scalable, and secure AI pipelines. This comprehensive guide bridges the gap between theory and real-world implementation, showing exactly how to craft reliable large language model applications using LangChain and OpenAI's powerful APIs.
What makes this book indispensable?
Foundations of LLM Workflow Engineering: Master prompt construction, chaining models, managing API keys, and securely setting up your development environment.
Effective Prompt Engineering: Create dynamic, template-driven prompts that consistently yield precise and contextually accurate outputs.
Building and Orchestrating Chains: Seamlessly combine multiple models and tools into cohesive, powerful workflows.
Working with LLM Memory and State: Manage token limits effectively, maintain conversation context, and scale conversational applications.
Retrieval-Augmented Generation (RAG) Pipelines: Boost accuracy and reduce hallucinations by integrating external knowledge bases and custom retrieval strategies.
Caching, Cost Control, and Performance Optimization: Learn proven methods for minimizing costs and latency without compromising output quality.
Error Handling, Resilience, and Observability: Ensure pipeline stability and reliability through advanced logging, tracing, and proactive monitoring.
Fine-Tuning and Custom Model Integration: Adapt models specifically to your data and domain, maximizing performance and precision.
Secure and Scalable Deployment: Confidently deploy LLM services at scale using FastAPI, Docker, Kubernetes, serverless architectures, and automated CI/CD pipelines.
Advanced Use Cases and Patterns: Implement complex solutions like retrieval-augmented conversational agents, multi-model workflows, external API integrations, and human-in-the-loop feedback systems.
Testing, Validation, and Continuous Improvement: Validate and benchmark models through rigorous unit tests, automated evaluations, A/B testing, and structured user feedback loops.
Written with clarity, practicality, and deep real-world insight, each chapter features detailed examples, comprehensive troubleshooting, and actionable tips designed for developers, data engineers, and AI professionals.
Ready to engineer powerful, reliable AI workflows that deliver real value?
Get your copy today and build AI solutions you can trust.
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