NEWS · CODING · #1527
InfoQ: Platform engineering playbook for production LLMs
InfoQ publishes a platform-engineering playbook based on a retail inventory recommendation system that reduced hallucination rate from ~15% to ~1.5% without changing the foundation model by applying platform-level controls (automated retry loops, intent-validation gates, prompt registries, tool authorization, and custom instrumentation). The article links a companion GitHub repo with runnable examples that use a local llama3.1 via Ollama connected through LiteLLM and describes routing through foundation models such as Gemini and GPT under enterprise traffic.
KEY POINTS
- InfoQ publishes a platform-engineering playbook based on a retail inventory recommendation system that reduced hallucination rate from ~15% to ~1.5% without changing the foundation model by applying platform-level controls (automated retry loops, intent-validation gates, prompt registries, tool authorization, and custom instrumentation).
- The article links a companion GitHub repo with runnable examples that use a local llama3.1 via Ollama connected through LiteLLM and describes routing through foundation models such as Gemini and GPT under enterprise traffic.
- Demonstrates that platform-level engineering, observability, and controls—not just changing foundation models—can materially reduce hallucinations and enable cost attribution in production LLM deployments.
WHY IT MATTERS
Demonstrates that platform-level engineering, observability, and controls—not just changing foundation models—can materially reduce hallucinations and enable cost attribution in production LLM deployments.