Prompt Monitoring with Helicone - Tracking LLM Requests in Production

ShopMax India uses multiple LLM API calls per customer session - product recommendations, chatbot responses, and search queries. Without visibility into these calls, debugging cost spikes or quality regressions is guesswork. Helicone acts as a transparent proxy between your Python app and the OpenAI API, logging every request and response with zero code changes beyond a URL swap.

Helicone captures the full prompt, completion, model name, token counts, latency, and estimated cost for every call. Requests are tagged with custom properties - feature name, user ID, session ID - enabling drill-down analysis in the Helicone dashboard. ShopMax India uses these tags to attribute API spend to specific product features and user segments.

The example below shows how ShopMax India wires Helicone into its product recommendation service. The only changes are the base_url and two extra headers.

It gives the following output,

Here are top laptops under Rs 60,000 for college use at ShopMax India:

1. Lenovo IdeaPad Slim 3 (Rs 45,990) - AMD Ryzen 5, 8GB RAM, 512GB SSD
2. HP Pavilion 15 (Rs 54,990) - Intel Core i5, 16GB RAM, 512GB SSD
3. ASUS VivoBook 15 (Rs 52,990) - AMD Ryzen 5, 8GB RAM, 512GB SSD

All three handle coursework, coding, and light video editing comfortably.

Tag every request with Helicone-Property-Feature to segment API costs by product area. Use Helicone-User-Id to trace quality issues back to specific user segments without exposing PII. Set a Helicone rate limit header to cap spend during load tests. Export logs to BigQuery via the Helicone API for long-term cost trend dashboards.