Overview
Welcome to our how-to guides for optimizing and fixing AI applications. These guides offer practical solutions to common problems across three key areas:
Each guide shows you how to spot problems, understand what metrics reveal, see examples of poor setups, and follow clear steps to fix issues.
Troubleshooting and Optimizing Your AI Applications
We’ve organized these guides by application type to help you solve specific problems in your AI systems. Whether you’re dealing with hallucinating models, inefficient agents, or poor retrievals, you’ll find practical advice based on metrics and proven fixes.
Choose the section that matches your current challenge, or explore all areas to build a better understanding of AI optimization.
Conversational AI
When AI chat systems give wrong information or unclear answers, users lose trust. Our guides help you fix these conversation problems using key metrics, so you can build AI that communicates clearly and accurately.
- Basic OpenAI Integration: Learn how to integrate and use OpenAI’s API with Galileo’s wrapper client.
- Instruction Adherence: Help your models better follow user instructions.
- Fixing Hallucinations and Factual Errors: Reduce made-up or incorrect information.
- Reducing Hesitation and Uncertainty: Create more confident and clear responses.
Retrieval-Augmented Generation (RAG)
When your RAG system can’t find the right information or use it properly, answers become less accurate. Our guides help you fix these knowledge retrieval problems so your AI can provide more accurate, relevant responses.
- Basic RAG Example: Learn how to implement a basic RAG system using Galileo and OpenAI.
- Preventing Out-of-Context Information: Keep irrelevant information out of responses.
- Ensuring Complete Use of Retrieved Data: Make sure your system uses all the information it finds.
- Maximizing Chunk Utilization: Improve how your model uses retrieved text.
- Fixing Irrelevant Retrievals: Make sure your system finds the most relevant information.
Agentic AI
When AI agents fail to complete tasks correctly, workflows break down and users lose confidence. Our guides help you identify why agents struggle and how to improve their ability to reason and complete complex tasks.
- Basic Agentic AI Example: Learn how to implement a basic agentic AI system using Galileo and OpenAI.
- Optimizing Multi-Step Task Execution: Help your agents complete complex tasks successfully.
- Ensuring Agents Follow Instructions: Improve how well agents follow directions.
- Improve Agent Decision Making: Help agents make better choices and follow logical reasoning.