Module 8 of 18
Retrieval-Augmented Prompting
Combine prompt engineering with external knowledge retrieval
Extending AI with Knowledge and Tools
While standalone LLMs are powerful, their true potential is unlocked when combined with external knowledge and tools. This module explores techniques that extend model capabilities beyond their training data and enable them to take actions in the world.
You will learn how to implement systems that retrieve relevant information on-demand, use specialized tools to solve tasks, and improve through observation and feedback.
The future of AI is not just about smarter models, but about intelligent systems that combine models with data, tools, and feedback mechanisms to solve real-world problems more effectively than any single component could alone.
Retrieval-Augmented Generation (RAG)
RAG combines the reasoning capabilities of LLMs with external knowledge retrieval to deliver more accurate, relevant, and up-to-date responses. Instead of relying solely on what the model learned during training, RAG systems dynamically access and incorporate information as needed.
Knowledge Base Creation
Document collection and preprocessing. Chunking text into manageable segments. Creating vector embeddings for each chunk. Storing in a vector database for retrieval.
Query Processing
Converting user query to vector embedding. Searching the vector database for relevant chunks. Ranking and selecting top results. Compiling retrieved context.
Response Generation
Crafting prompt with retrieved context. Instructing the LLM how to use the context. Generating a response based on context and query. Formatting and delivering the final answer.
Continuous Improvement
Monitoring retrieval quality. Tracking response relevance and accuracy. Refining retrieval algorithms. Updating the knowledge base.
RAG systems combine the best of both worlds: the factual accuracy and recency of a knowledge base with the flexibility and reasoning capabilities of an LLM, enabling more reliable, verifiable responses with explicit sources.
Prompt
Output
LLM Agents and Tool Use
LLM agents are systems that can use tools to interact with the world and solve tasks. By giving language models the ability to use specialized tools (like calculators, search engines, or APIs), we enable them to accomplish tasks beyond text generation.
The true power of LLMs emerges when they can choose and use appropriate tools to accomplish tasks. This transforms them from passive text generators into active problem-solvers capable of interacting with the world.
- 01
Tool Collection
A set of specialized functions the agent can call to perform specific tasks, each with a description, parameters, and expected outputs. - 02
Planning Module
Analyzes user requests, breaks them into steps, and determines which tools to use and in what sequence to accomplish the goal. - 03
Execution Engine
Calls tools with appropriate parameters, handles their outputs, and manages state between steps of multi-step processes. - 04
Memory System
Stores previous interactions, tool results, and intermediate states to maintain context throughout multi-step tasks. - 05
Communication Interface
Translates between the user's natural language and the agent's internal processes, explaining actions, requesting clarification, and presenting results.
Prompt
Output
Observation-Based Improvement
Observation-based systems go beyond static prompting by incorporating feedback and learning from interactions. They track performance, collect observations, and adapt their behavior to improve over time.
The most effective AI systems do not just execute instructions. They learn from their experiences. By collecting observations and adapting behavior based on feedback, these systems continuously improve their performance and better meet user needs.
Feedback Collection
Explicit user ratings and reviews. Implicit signals (clicks, time spent, etc.). Success and failure metrics for tasks. User corrections and clarifications. A/B testing different approaches.
Adaptation Strategies
Prompt refinement based on performance. Tool selection optimization. User preference modeling. Self-critique and reflection. Memory-augmented interactions.
Prompt
Output
Combining RAG, Agents, and Observation
The most powerful systems integrate all three approaches:
- 01
RAG provides up-to-date knowledge
Retrieves relevant information to inform decision-making. - 02
Agents take actions based on this knowledge
Use tools to accomplish tasks informed by retrieved information. - 03
Observation systems improve both components
Refine retrieval strategies and agent behaviors based on outcomes.
Activities
Activity 1
45-60 min · Advanced
RAG Implementation
Implement a basic RAG system using a vector database and LLM to answer questions about a specific domain.
Activity 2
40-50 min · Advanced
Agent Design
Design and implement a simple agent that can use tools to solve a specific task.
Activity 3
50-60 min · Advanced
Observation-Based System
Create a system that uses observations to improve its responses over time.
Additional Resources
- Research Paper: "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks"arxiv.org
- Research Paper: "ReAct: Synergizing Reasoning and Acting in Language Models"arxiv.org
- LangChain: Framework for Building LLM Applicationslangchain.com
- Pinecone: Practical Guide to Retrieval Augmented Generationpinecone.io