The AI & LLM Integration Cookbook (part-1)

The AI & LLM Integration Cookbook

10 Production-Ready Python Templates for Developers

Copy. Paste. Adapt. Ship.

Stop starting every AI integration from scratch.

This practical cookbook gives you 10 complete Python recipes for common AI and LLM development patterns, with runnable code you can adapt to your own projects.

What's inside?

You'll get practical templates covering:

1. OpenAI Chat Completions

Build both standard and streaming LLM calls.

2. Anthropic Claude Messages API

Implement synchronous and streaming Claude interactions.

3. OpenAI Tool / Function Calling

Connect an LLM to real Python functions and external data sources.

4. Anthropic Claude Tool Calling

Implement Claude's tool-use pattern with multiple tool calls.

5. Dynamic System Prompt Builder

Generate system prompts dynamically using roles, constraints, tone, and examples.

6. RAG over Private PDFs with LangChain

Load, chunk, embed, index, and query private PDF content.

7. RAG over Private PDFs with LlamaIndex

Build a similar document-questioning workflow using LlamaIndex.

8. Multi-Turn Conversational Memory

Maintain conversation history while automatically trimming older turns to manage context.

9. Embeddings + Vector Search with FAISS

Work directly with embeddings and similarity search without relying on a full RAG framework.

10. Production API Wrapper

Add retries, rate-limit handling, logging, exponential backoff, and fallback models.

Why this cookbook?

Every recipe is designed as a complete runnable file, rather than a fragment of code.

You'll also find:

✓ Required pip installation commands

✓ Environment-variable API key setup

✓ Practical implementation examples

✓ Short explanations of the design patterns

✓ Production-oriented considerations such as retries, streaming, logging, and context management

✓ Recipes you can combine to build more complete AI applications

Who is it for?

This cookbook is for developers who want practical examples for integrating AI and LLM capabilities into Python applications.

If you prefer building with code rather than reading pages of theory, this cookbook is designed for you.

Build from working patterns

You can combine the recipes to create more capable applications—for example, using RAG with conversational memory and production reliability patterns.

The goal isn't to give you another collection of AI concepts.

It's to give you reusable starting points you can adapt to your own projects.

What you receive

📘 The AI & LLM Integration Cookbook — PDF

10 practical Python recipes for AI & LLM integration.

Get the Cookbook

Start building with AI and LLMs today.