Session 5 · Tools + Choosing the Approach · TCE · 50 min · pairs · Same rhythm.
Runs entirely in your browser (Google Colab). Any laptop or lab PC works — nothing to install, no GPU, no payment. See
ZERO-SETUP.
Define calculator as a plain Python function with a real docstring. Hand it to the model. Ask the GST question — exact answer. Then ask without the tool and watch it guess. That contrast is the lesson: the model delegates instead of predicting digits.
✓ Checkpoint 1: with-tool exact, no-tool wobbles.
Add days_between. Ask something needing both (the internship question). The agent loop chains them — nobody scripted the order.
✓ Checkpoint 2: both tools called in one answer.
Disable automatic calling; print the raw function_call the model emits. This is the request; your code decides whether to run it. That's why the model can't actually do anything dangerous by itself.
Four scenarios → pick Prompt / RAG / Tools / Fine-tune, justify in 2 sentences each. A mini design doc — and capstone rehearsal.
✓ Checkpoint 3: defend one choice to me out loud.
Plug your Session 4 search_notes in as a tool — your capstone becomes a real assistant (knowledge + hands) · manual agent loop · break-it-then-fix-the-docstring (proves docstrings are prompts).
Going deeper (S3, S4 in the notebook). Grade the trajectory — score which tool it reaches for first, including the questions that need no tool; over-calling is as much a bug as missing a call, and this number drops the moment a docstring goes vague. Put the loop on a leash — step cap, tool allow-list, validated arguments, readable errors. That guarded loop, not the bare while, is what production looks like. Deck: press D on “The agent loop”.
You now hold all three pieces: RAG (knowledge) + tools (hands) + evals (judge). Save the notebook. The finale attacks it, you harden it, then demo. Bring it charged.