Lessons from building an AI data analyst
9 days ago
- #Semantic Layer
- #AI Data Analyst
- #Text-to-SQL
- Text-to-SQL alone is insufficient for real user questions; multi-step plans, external tools, and context are necessary.
- A semantic layer (like Malloy) encodes business meaning, reducing SQL complexity and improving reliability.
- Multi-agent, research-oriented systems break down problems, retrieve precisely, write code, and learn from interactions.
- Retrieval should be treated as a recommendation problem, mixing keyword search, embeddings, and fine-tuned rerankers.
- User expectations in production go beyond benchmarks, requiring human-level answers, drill-downs, and defensible reasoning.
- Latency and quality are critical; route between fast and reasoning models, cache aggressively, and keep contexts short.
- Context engineering and semantic metadata are crucial for accurate AI-powered data tools.
- Python code generation is essential for post-SQL computations, leveraging libraries for efficiency and correctness.
- Multi-agent planning, memory, and grounding reduce hallucinations and improve accountability.
- Fine-tuned instruction-following rerankers optimize retrieval for LLMs, improving precision and recall.