How to Build an AI Copilot – From Zero to Hero

Join Lumigo as we take you on the journey of transforming Lumigo Copilot from an idea into an expert AI-powered observability problem-solver. You’ll learn about some of the early challenges we faced working with a large language model that often missed the mark and how our initial attempts, grounded in basic retrieval-augmented generation (RAG), left the model with little structure, leading to frequent hallucinations and inconsistent results.

We’ll show you how advanced prompt engineering taught Lumigo Copilot to think in structured steps, progressing from zero-shot to few-shot and ultimately incorporating agent-based architecture to achieve reliable performance. You’ll gain an insider’s look at the systematic improvements we applied, from guiding the model like a junior developer to setting up an evaluation pipeline that allowed us to monitor and scale effectively.

What You’ll Learn:

• How to Guide the LLM Like a Junior Developer – Discover how domain expertise and intentional guidance transformed our initial failed attempts into a problem-solving solution for complex observability issues.
• Advanced Prompt Engineering Techniques – Explore our journey from zero-shot to few-shot, chain-of-thought (CoT), and task-oriented thought (ToT) prompting to improve accuracy and model understanding.
• Evaluating and Scaling for Performance – Learn how we established an evaluation pipeline to track baseline performance, iteratively improved results, and reached 70% accuracy across test cases with an agent-based approach.

Watch now to gain actionable insights into creating a high-performance copilot, overcoming LLM challenges, and leveraging AI to streamline complex problem-solving.