Ready Tensor Agentic AI Certification - Week 3

This repository contains the lessons, practical code and exercises for Week 3 of the Agentic AI Developer Certification Program by Ready Tensor. Week 3 builds on the modular prompt engineering concepts introduced in Week 2 and adds practical examples of multi-turn conversations and robust system prompts.

What You'll Learn

• Making your first LLM calls and grounding them in publication content
• Designing safe, consistent, and modular system prompts
• Managing chat memory in long conversations
• Adding memory persistence for session continuity
• Implementing a full RAG pipeline using vector databases and embeddings

Lessons in This Repository

1. Your First LLM Calls: Getting Started with Groq & LangChain

Make basic LLM calls using LangChain and Groq. Learn to ground questions in documents and maintain context in multi-turn conversations.

2. System Prompts: Your AI’s Operating Manual

Design modular system prompts that define assistant behavior, tone, and safety boundaries. See how prompt structure impacts reliability and security.

3a. Memory Management Strategies: When Conversations Get Too Long

Explore how to manage growing conversation history using three strategies: stuffing, recent-only, and summarization — and analyze their trade-offs.

3b. Memory Persistence: Saving Conversations Across Sessions

Add memory persistence to your assistant so users can resume chats across sessions. Covers LangChain’s memory types and custom database implementations.

4. Building Your Research Assistant: A Step-by-Step RAG Implementation

Build a complete RAG system that semantically retrieves publication content and uses it to generate grounded, context-aware responses in real time.

Repository Structure

rt-agentic-ai-cert-week3/
├── code/
│   ├── config/
│   │   ├── config.yaml                 # App config
│   │   └── prompt_config.yaml          # Prompt configurations for system prompt examples
│   ├── paths.py                        # File path configurations
│   ├── prompt_builder.py               # Modular prompt construction functions
│   ├── run_wk3_l1_example_1_2.py       # Lesson 1: Basic LLM calls and publication grounding
│   ├── run_wk3_l1_example_3.py         # Lesson 1: Interactive terminal chat example
│   ├── run_wk3_l2_sys_prompt_example.py # Lesson 2: System prompt examples and testing
│   ├── run_wk3_l3a_memory_strategies.py # Lesson 3A: Memory strategies comparison
│   ├── run_wk3_l4_vector_db_ingest.py  # Lesson 4: Vector DB ingestion script
│   ├── run_wk3_l4_vector_db_rag.py     # Lesson 4: Vector DB RAG example
│   └── utils.py                        # Utility functions
├── data/                               # Sample publications for exercises
│   ├── 57Nhu0gMyonV.md
│   ├── ljGAbBceZbpv.md
│   ├── tum5RnE4A5W8.md
│   ├── yzN0OCQT7hUS-sample-questions.yaml  # Sample questions used in lesson 3 exercise
│   └── yzN0OCQT7hUS.md
├── lessons/                            # Lesson content and exercises
├── outputs/                            # Generated prompts and LLM responses
├── .gitignore
├── LICENSE
├── README.md
└── requirements.txt

Installation & Setup

1. Clone the repository:

   git clone https://github.com/readytensor/rt-agentic-ai-cert-week3.git
   cd rt-agentic-ai-cert-week3

2. Install dependencies:

   pip install -r requirements.txt

3. Set up your Groq API key:

   Create a .env file in the root directory and add your API key:

   GROQ_API_KEY=your-api-key-here
   

   You can get your API key from Groq.

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Running the Lessons

This repository includes scripts to help you experiment with different types of interactions and prompt configurations.

Lesson 1 — Basic LLM Calls & Multi-Turn Conversations

• run_wk3_l1_example_1_2.py

  • Example 1: A simple LLM call answering a general question about VAEs.
  • Example 2: The same question, but grounded in the publication content.

• run_wk3_l1_example_3.py

  • An interactive terminal-based chat with the assistant, showing how multi-turn conversations work and how context is managed.

Lesson 2 — System Prompts for Control & Safety

• run_wk3_l2_sys_prompt_example.py

  • Tests system prompt configurations (basic and advanced) to see how they handle manipulative questions and maintain professional tone and safety.

Lesson 3A — Memory Management Strategies

• run_wk3_l3a_memory_strategies.py
  • Compares three memory management strategies: stuffing everything, trimming to recent messages, and summarizing conversation history.
  • Simulates a long conversation using real questions, saving detailed results (Q&A pairs, token usage, and final prompts) in the outputs/ directory.
  • Includes an interactive mode for running a single strategy or a full comparison report.

Lesson 4 — Vector Database & RAG Implementation

• run_wk3_l4_vector_db_ingest.py

  • Vector Database Ingestion: Initializes a ChromaDB instance with persistent storage, chunks publications into smaller documents, embeds them using HuggingFace transformers, and stores them in the vector database for semantic search.

• run_wk3_l4_vector_db_rag.py

  • Retrieval-Augmented Generation (RAG): Interactive terminal-based chat that retrieves relevant documents from the vector database based on user queries and generates contextual responses using retrieved content. Includes configurable similarity thresholds and result counts.

Each script saves outputs and transcripts in the outputs/ directory for easy review and comparison.

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License

This project is licensed under the CC BY-NC-SA 4.0 License – see the LICENSE file for details.

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Contact

Ready Tensor, Inc.

• Email: contact at readytensor dot com
• Issues & Contributions: Open an issue or pull request on this repository
• Website: Ready Tensor