Human-in-the-Loop AI System
Budget: $30 – $250 USD
I’m rolling out the first iteration of a human-in-the-loop layer for our Python-based TensorFlow/Keras agents. The goal is simple: whenever an agent detects uncertainty or cannot satisfy a query, it should
• flag the event in real time
• route the conversation—including full context—to a human supervisor
• capture the supervisor’s response, send it back to the customer seamlessly, and
• fold the new answer into the knowledge base so the model improves automatically.
Scope of work
1. Uncertainty detection – integrate confidence-threshold logic (or an equivalent entropy measure) inside our existing TensorFlow/Keras pipeline.
2. Escalation flow – spin up a lightweight API (FastAPI or Flask is fine) that hands off the conversation to a human. A simple web dashboard with login, message thread, and “commit back” button is enough for this version.
3. Feedback loop – once the supervisor submits an answer, push it to the customer using the current channel (REST endpoint provided) and store the Q&A pair in our vector store so future queries hit instantly.
4. Automated retraining hook – schedule a nightly job that ingests the day’s approved answers and triggers our training script; no manual intervention should be required.
5. Logging & metrics – persist every handoff, response time, and model update so we can track accuracy lift over time.
Acceptance criteria
• All four paths—AI answer, AI-to-human escalation, supervisor reply, and KB update—work end-to-end in a local Docker setup.
• Unit tests cover the escalation trigger and retraining hooks.
• README explains how to spin up the stack, set thresholds, and add supervisors.
The current codebase is clean Python 3.10 with TensorFlow 2.x; feel free to suggest pragmatic libraries as long as they stay in the Python ecosystem.
• flag the event in real time
• route the conversation—including full context—to a human supervisor
• capture the supervisor’s response, send it back to the customer seamlessly, and
• fold the new answer into the knowledge base so the model improves automatically.
Scope of work
1. Uncertainty detection – integrate confidence-threshold logic (or an equivalent entropy measure) inside our existing TensorFlow/Keras pipeline.
2. Escalation flow – spin up a lightweight API (FastAPI or Flask is fine) that hands off the conversation to a human. A simple web dashboard with login, message thread, and “commit back” button is enough for this version.
3. Feedback loop – once the supervisor submits an answer, push it to the customer using the current channel (REST endpoint provided) and store the Q&A pair in our vector store so future queries hit instantly.
4. Automated retraining hook – schedule a nightly job that ingests the day’s approved answers and triggers our training script; no manual intervention should be required.
5. Logging & metrics – persist every handoff, response time, and model update so we can track accuracy lift over time.
Acceptance criteria
• All four paths—AI answer, AI-to-human escalation, supervisor reply, and KB update—work end-to-end in a local Docker setup.
• Unit tests cover the escalation trigger and retraining hooks.
• README explains how to spin up the stack, set thresholds, and add supervisors.
The current codebase is clean Python 3.10 with TensorFlow 2.x; feel free to suggest pragmatic libraries as long as they stay in the Python ecosystem.