Memory, Summarisation & Alignment Scoring
Overview
Give your teaching pipeline long-term memory with summarisation,
then measure every AI reply using a three-axis alignment scorer — before it reaches the learner.
Prerequisites
Completed Tutorial 1 — prompt builder, conversation wrapper, sliding window
Completed Tutorial 2 — Flask API, session-based memory, chat UI [https://aplly.xyz/tutorial/building-a-flask-chat-ui-with-a-non-chat-hf-model]
Comfortable reading Python functions with multiple return values
Basic understanding of what "alignment" means in AI contexts
Learning Outcomes
Distinguish three memory strategies — sliding window, summarisation, and hybrid — and choose the right one
Write a summarisation function that condenses old turns using the LLM itself
Implement a three-axis alignment scorer (helpfulness, safety, relevance) that runs before replies reach learners
Build a complete teaching pipeline that chains memory + scoring + gating logic
Apply a log-and-review system for flagged low-quality or unsafe responses