Hands-On-LLMs: A Practical Guide to Large Language Models and Agentic AI

Hands-On-LLMs is an open-source GitHub project maintained by Aniket Patil, focusing on practical implementations and experiments of Large Language Models (LLMs) and Agentic AI. The project covers core technical directions such as Retrieval-Augmented Generation (RAG), AI agents, workflow orchestration, prompt engineering, tool integration, memory architectures, and autonomous reasoning frameworks, providing developers with a complete learning path from basics to advanced levels, making it a valuable resource for modern AI development.

Original Author and Source

• Original Author/Maintainer: AniketP04 (Aniket Patil)
• Source Platform: GitHub
• Original Title: Hands-On-LLMs: Practical implementations and experiments in LLMs and Agentic AI
• Original Link: https://github.com/AniketP04/Hands-On-LLMs
• Update Time: 2026-06-14T10:09:02Z

Project Overview

Hands-On-LLMs is a comprehensive open-source project focusing on practical implementations and experiments of LLMs and Agentic AI. It provides developers with a complete learning path from basics to advanced levels, covering popular technical directions in the current AI field, and is a valuable resource for learning and practicing modern AI development.

Module 1: FactFinder AI

Focuses on fact-checking and knowledge retrieval systems, demonstrating RAG technology applications:

• Technical Points: Document indexing and vector storage, semantic search and relevance ranking, integration of retrieval results with generation, fact accuracy verification mechanism
• Learning Value: Understand core components of RAG systems (knowledge base preparation, retrieval strategy design, integration of retrieval results into generation)

Module 2: Hands-On-LangChain

Provides LangChain practical tutorials:

• Covered Content: Chain construction and combination, Agent design and implementation, Memory component usage, Tools integration and customization, Prompt template engineering
• Practical Significance: Quickly build complex LLM applications (from question-answering bots to multi-step reasoning agents)

Module 3: LLM Fine-Tuning Pipeline

Complete LLM fine-tuning implementation (from data preparation to model deployment):

• Technical Flow: Dataset preparation and cleaning, tokenization and data encoding, training configuration and hyperparameter tuning, distributed training support, model evaluation and export
• Application Scenarios: Domain adaptation, task specialization, style customization

RAG System Architecture

Typical Flow: User Query → Query Understanding → Vector Retrieval → Re-ranking → Context Construction → LLM Generation → Post-processing Covers: Embedding model selection, vector database selection and usage, retrieval result fusion strategy, context window optimization

Agentic AI Design Patterns

• ReAct Pattern: Alternating reasoning and action to solve complex problems step by step
• Plan-and-Execute Pattern: Formulate a plan first then execute steps, suitable for multi-step coordination tasks
• Multi-Agent Collaboration: Multiple specialized agents work together

Memory Architecture Implementation

• Short-term Memory: Maintenance of current conversation window context
• Long-term Memory: Persistence of cross-session information
• Entity Memory: Extraction and tracking of key entities
• Summary Memory: Compression and summarization of long conversations

Technology Stack Analysis

• Main Language: Python (90.3%)
• Dependencies Ecosystem: LangChain (LLM application framework), Transformers (Hugging Face model library), Vector DBs (vector databases), PyTorch/TensorFlow (deep learning frameworks)

Community Contributions

As an emerging open-source project, community contributions are welcome: submit new implementation examples, improve existing code, perfect documentation, share usage experiences

Core Values of Hands-On-LLMs:

1. Comprehensiveness: Covers multiple key areas of LLMs and Agentic AI
2. Practicality: Provides runnable code examples, not just pure theory
3. Structured: Modular organization for on-demand learning
4. Timeliness: Covers the latest cutting-edge technical directions

It is an ideal starting point for systematic learning of modern AI development.

Path 1: RAG Application Developer (Suitable for Building Enterprise Knowledge Base Q&A Systems)

1. Learn the FactFinder AI module
2. Master vector database usage
3. Practice retrieval strategy optimization
4. Deploy production-level RAG systems

Path 2: Agent System Architect (Suitable for Building Complex AI Agents)

1. Dive into the Hands-On-LangChain module
2. Master Agent design patterns
3. Practice tool integration
4. Build multi-agent collaboration systems

Path 3: Model Customization Engineer (Suitable for Fine-Tuning Models to Meet Specific Needs)

1. Learn the LLM-Fine-Tuning-Pipeline
2. Master data preparation skills
3. Practice training and evaluation
4. Deploy customized models