Lesson 4 — How to Co-Think with Intelligence
Learning Outcome: Learn to collaborate with models: prompt design, feedback loops, and conversational reasoning using the Foundry /query endpoint.
Manual
The Bridge of Intention
To co-think is to share the act of reasoning. When we prompt an LLM, we do not issue commands — we open resonance between human intent and synthetic inference. The precision of that resonance depends on clarity of intention and awareness of feedback.
1 · The Dialogue Loop
Every exchange forms a feedback circuit: Prompt → Response → Reflection → Revision.Each loop tightens alignment, mirroring two oscillators finding phase. True collaboration arises not from control but from listening to the pattern between.
2 · The Prompt as Instrument
A prompt is a tuning curve, not an order. Word choice, tone, and example layout shape the model's internal field of attention. Fine-tuning begins with phrasing; structure becomes outcome.
- ML: context window, instruction tuning
- Cognitive: intentionality directs focus
- Philosophy: dialogue as creation
3 · Few-Shot Alignment
Examples teach pattern without retraining. A handful of demonstrations—inputs and ideal outputs—define local behavior. This is few-shot learning, the art of instructing by analogy.
4 · Chain-of-Dialogue
Iteration deepens context. With every turn, the model's attention re-weights meaning; our questions refine it further. Shared reasoning emerges from continuity, not command.
Workshop
Hands-On: Prompt Resonance
Goal: experience how small changes in wording alter the model's reasoning.
Prompt engineering playground placeholder
(Full interactive demo available at brainfoundry.ai)
How to use
- Modify the instruction ("summarize," "rephrase poetically," "explain like a scientist")
- Send → observe how tone and focus shift
- Add a short feedback line ("make it clearer") → send again
API Usage
curl -X POST /api/foundry/query \
-H "Content-Type: application/json" \
-d '{ "prompt": "Summarize the paragraph precisely.", "input": "Intelligence is the ability to find structure in uncertainty and act accordingly." }'Optional Feedback Endpoint
After a good response:
curl -X POST /api/foundry/feedback \
-H "Content-Type: application/json" \
-d '{ "prompt_id": 42, "rating": "useful", "notes": "Clear, concise summary" }'Use the feedback signal to tune later responses or future training rounds.
Reflection
Reflection — Asking as Art
How does your way of asking shape what you receive? Write 5–8 sentences describing one time you changed how you asked (a person or a model) and how the outcome changed. Consider whether clarity, tone, or curiosity mattered most.