10 Ways to Use Your LLM Logs to Build Better AI Products
Blog post from Helicone
Helicone's data export tool was developed to meet the demand from teams seeking to enhance AI products by leveraging raw production data, allowing for fine-tuning models, building custom dashboards, and analyzing user patterns. The tool provides a simple CLI command for exporting logs in formats such as JSON, JSONL, or CSV, supporting extensive filtering options and featuring built-in retry logic for efficient handling of large data sets. Helicone outlines ten strategic ways to utilize these logs, including fine-tuning models with real data, conducting advanced statistical analysis, calculating unit economics, analyzing semantic patterns, creating synthetic test data, developing custom embedding models, establishing automated regression testing suites, mapping user journeys, constructing ROI models, and building real-time dashboards. By treating observability data as a vital resource, teams can optimize their AI products for improved performance, cost-efficiency, and user engagement, ultimately driving increased retention and competitive advantage.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| LLM | 12 | 5,048 | 855 | 225 | +5% |
| Vector Search | 6 | 1,541 | 318 | 153 | -17% |
| AI Model Fine-tuning | 3 | 470 | 151 | 72 | -14% |
| Observability | 2 | 3,012 | 601 | 171 | +15% |
| RAG | 2 | 1,167 | 195 | 86 | +2% |
| Real-time | 1 | 5,379 | 1,225 | 279 | -24% |
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.