Building an AI solution often starts with prompt engineering. When the results are disappointing, many teams simply keep adjusting prompts and hoping for better outcomes. In real enterprise scenarios, especially when working with legal and regulatory content, this approach quickly reaches its limits.

Using real-world lessons learned from building AI solutions on large collections of legal documents, this session explores a structured approach to improving answer quality. We will look at common failure modes, why prompt engineering alone is often insufficient, and how techniques such as retrieval augmentation, metadata enrichment, re-ranking, query expansion, and agent-based workflows can dramatically improve business outcomes.

Rather than focusing on a single technology, the session presents a practical framework for diagnosing quality issues and choosing the right architectural response.

Key Takeaways
• Learn how to systematically analyze poor AI responses
• Understand when prompt engineering is no longer the right solution
• Discover architectural patterns that improve answer quality
• Learn practical lessons from real-world legal knowledge systems

Target audience:
Developers, AI Engineers, Architects and Technical Decision Makers interested in improving AI solution quality beyond prompt engineering

Session duration:
45 minutes