In the news
Benchmarking Question/Answering Over CSV Data
LangChain · Published · 3 min read
In 30 seconds
- What happened
- LangChain published a deep dive on building question-answering systems over CSV data, covering evaluation methods and an improved agent-based solution.
- Why it matters
- Engineers building natural language interfaces over tabular data need this when evaluating LLM applications and debugging data formatting issues in production.
- Watch out
- The solution was tested on a single dataset (Titanic), which may not represent all CSV structures, sizes, or question types users encounter in practice.
Listen to this summary
- agent
- llm
- retrieval
- eval
- benchmark
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.