Subchapter 24.81
references/planning/overview.mdMarkdown8 KBView on GitHub
Goal: Understand what the user wants to accomplish and identify which references belong in the plan.
Read references/input-output-contracts.md, references/model-customization-plan.md, references/evaluate-first-plan.md, and references/deploy-base-model-plan.md to:
During brainstorming:
Workflow choice gate: Before generating any plan, determine which workflow the user needs. There are three paths:
Disambiguation for “deploy”: If the user says “I want to deploy a model” (or similar), first determine whether they want to deploy a fine-tuned model they already have, or deploy a base model as-is:
model-deployment reference will handle it as a later step.references/deploy-base-model-plan.md.If the user has explicitly chosen a path (e.g., “evaluate first”, “skip evaluation”, “deploy a base model”, “I already fine-tuned”), proceed with their choice. Otherwise, present the relevant options with brief pros/cons and ask the user to choose. Saying “fine-tune” or naming a technique alone is NOT an explicit choice to skip evaluation — the user may not know evaluate-first is an option. Do NOT present a plan until the user has chosen a path. After they choose, read ONLY the corresponding reference plan.
Use the Restrictions column of the contracts table to flag constraints as soon as the relevant decision is made. Examples (non-comprehensive list, check contracts table for the full picture):
If a restriction applies, check whether it requires changes to other steps in the plan.
Do NOT ask the user about base model selection or preferences. Model selection is handled exclusively by the model-selection reference.
Move to Phase 2 as soon as you can determine which references and tools the plan needs.
Goal: Propose a structured plan for the user to review.
Generate a plan as a numbered list of tasks. Each task has:
Format:
Based on what you've described, here's what I propose:
1. ⬜ **[Task Name]** — [What happens]. *(Reference: [reference-name])*
2. ⬜ **[Task Name]** — [What happens]. *(Reference: [reference-name])*
3. ⬜ **[Task Name]** — [What happens]. *(Reference: [reference-name])*
Does this plan look right, or would you like to change anything?
Rules for plan generation:
references/skill-routing-constraints.md.When the user approves the plan, write it to PLAN.md and save it under the project directory structure defined by the directory-management reference.
# Plan
1. ⬜ **[Task Name]** — [Description]. _(Reference: [reference-name])_
2. ⬜ **[Task Name]** — [Description]. _(Reference: [reference-name])_
3. ⬜ **[Task Name]** — [Description]. _(Reference: [reference-name])_
Status indicators:
Update PLAN.md whenever a task’s status changes.
Goal: Refine the plan until the user approves it.
Once the plan is approved:
PLAN.md to 🔄 (In Progress).Update its status in PLAN.md to ✅ (Completed). If the task generated output files (scripts, notebooks, manifests), record the file paths under the completed task:
- [x] Fine-tune model
- Output: `scripts/01_sft_finetuning.py`
- Output: `manifests/sft-llama-20260515.json`
Briefly confirm completion and move to the next task.
When all tasks in the plan are done: Present to the user:
“We’ve completed everything in the plan. What would you like to do next?”
This re-enters Phase 1 (Brainstorming) for a new goal. There is no terminal state — the conversation continues as long as the user wants.
Load the reference plan that matches the customer’s intent, then adjust based on their needs.
references/evaluate-first-plan.md — The evaluate-first workflow: evaluate a base model before deciding whether to fine-tune.references/model-customization-plan.md — The direct fine-tuning plan. Use when the user has explicitly committed to fine-tuning.references/deploy-base-model-plan.md — The deploy-base-model workflow: select and deploy a base model without fine-tuning.references/input-output-contracts.md - A table showing all references, required inputs, produced outputs, prerequisites, and constraints.references/skill-routing-constraints.md — Optional supplemental resource about Mandatory inclusion rules, ordering constraints, and skill boundary rules.