Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

The strongest enterprise AI products are rarely the ones that launch with the biggest model on day one—they are the ones that learn fastest from daily user interactions and corrections. Simply dropping a thumbs-up/down icon below a chat bubble is rarely enough when most users never click it. This course teaches how to engineer high-signal feedback loops and a continuous Data Flywheel.

Participants build a pipeline that links explicit and implicit user signals to trace IDs, routes problematic interactions into human correction queues, and converts verified fixes into dynamic few-shot examples, knowledge-base updates, golden eval cases and fine-tuning datasets.

Who Should Attend?

  • Software and AI engineers building LLM-powered products and internal assistants
  • Technical product managers designing continuously improving AI user experiences
  • LLMOps and data science teams building data flywheels and evaluation pipelines
  • Prerequisite: Python, basic LLM application architecture and tracing familiarity

Key Takeaways

  • Design high-participation explicit feedback UI patterns and frictionless implicit telemetry
  • Attach user feedback events directly to the corresponding traces and spans in Langfuse or Phoenix
  • Derive satisfaction scores from implicit signals such as edit distance, copy actions, re-queries and abandonment
  • Set up triage and expert correction queues for low-scoring production interactions
  • Transform verified human corrections into golden eval test cases, dynamic few-shot pools and SFT/DPO datasets

Curriculum

Designing Explicit and Implicit Feedback Signals
  • Moving beyond sparse thumbs-up/down buttons with structured reason tags and inline edit workflows
  • Implicit telemetry: suggestion acceptance, edit distance, citation clicks and session abandonment
Trace-Linked Telemetry and Event Ingestion
  • Exposing trace_id and message_id to the frontend for asynchronous feedback submission
  • Unifying user feedback and automated scores via the Langfuse and Arize Phoenix Score APIs
Triage, Clustering and Human-in-the-Loop Correction Queues
  • Clustering negative-feedback traces by root cause (missing doc, prompt flaw, model limitation)
  • Lightweight review workflows where subject-matter experts edit and approve gold responses
Rapid Iteration: Dynamic Few-Shot and Knowledge-Base Fixes
  • Injecting verified (query -> corrected answer) pairs immediately as dynamic few-shot examples
  • Routing recurring knowledge gaps to document owners to fix RAG source content
The Long-Term Data Flywheel: Evals, SFT and DPO Datasets
  • Promoting corrected production failures automatically into the golden regression suite
  • Exporting privacy-scrubbed (rejected response, expert correction) pairs for SFT and DPO fine-tuning

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