🤖 LLM Application Development

A hands-on learning repository focused on building Large Language Model (LLM) applications with Python.

This repository progresses from fundamental LLM application concepts to a complete end-to-end LLM Application Capstone, covering structured outputs, prompt engineering, document processing, embeddings, semantic search, RAG, evaluation, conversation memory, tools, and application routing.

---

🎯 Learning Objectives

This repository focuses on understanding how modern LLM applications are designed and implemented.

By completing this repository, the following concepts are covered:

• LLM Application Fundamentals
• LLM API and Model Interaction
• Structured Outputs
• Prompt Engineering
• Document Processing
• Text Chunking
• Embeddings
• Semantic Search
• Vector Stores
• Retrieval-Augmented Generation (RAG)
• LLM Evaluation
• Conversation Memory
• Tool Calling
• Data Analysis Tools
• Calculator Tools
• Text Summarization
• LLM Application Routing
• End-to-End LLM Applications

---

📚 Repository Structure

01 — LLM Application Fundamentals

Introduction to the basic architecture of an LLM application.

Topics:

• User input
• Prompts
• LLM responses
• Response processing
• Basic application pipelines

---

02 — LLM APIs & Model Interaction

Understanding how an application interacts with a model.

Topics:

• Model configuration
• Model requests
• Model responses
• Model clients
• API-style application architecture

---

03 — Structured Outputs

Learning how to produce consistent, machine-readable model responses.

Topics:

• Structured prompts
• Structured responses
• JSON output
• Mock structured LLMs
• Structured applications

---

04 — Advanced Prompt Engineering

Exploring different prompting strategies.

Topics:

• Zero-shot prompting
• Few-shot prompting
• Reasoning-oriented prompts
• Role-based prompting
• Advanced prompt applications

---

05 — Document Processing

Preparing documents for use in AI applications.

Topics:

• Document loading
• Text cleaning
• Text chunking
• Document pipelines
• Processed document applications

---

06 — Production RAG

Building a retrieval-based AI application.

Topics:

• Text embeddings
• Sentence Transformers
• Semantic search
• Cosine similarity
• Vector stores
• Context retrieval
• RAG pipelines

---

07 — LLM Evaluation

Measuring the quality of generated answers.

Topics:

• Evaluation datasets
• Answer similarity
• Semantic similarity
• Pass/fail evaluation
• Average similarity
• Evaluation metrics

---

08 — Conversation Applications

Building applications that maintain conversation history.

Topics:

• Conversation state
• Conversation context
• Memory
• Conversation agents
• Conversation summaries

---

09 — Complete LLM Application

Combining multiple capabilities into one application.

Features:

• Knowledge search
• Calculator
• Data analysis
• Conversation memory
• Request routing
• Direct answers

---

🏆 10 — Final LLM Application Capstone

The final project combines the major concepts from the repository into one application.

Capstone Architecture

                         USER
                           │
                           ▼
                  LLM APPLICATION
                           │
                           ▼
                       DECISION
                           │
        ┌──────────────────┼──────────────────┐
        │                  │                  │
        ▼                  ▼                  ▼
       RAG            CALCULATOR         DATA ANALYSIS
        │                  │                  │
        └──────────────────┼──────────────────┘
                           │
                    ┌──────▼──────┐
                    │ SUMMARIZER  │
                    └──────┬──────┘
                           │
                           ▼
                         MEMORY
                           │
                           ▼
                    FINAL RESPONSE