A solid prompting structure for grok-code-fast-1 is goal-first, context-scoped, and agentic: define the objective, supply only the necessary code/docs, specify constraints and interfaces, and iterate rapidly in short loops leveraging its speed and low cost. Below is a consolidated, working guide distilled from official docs and community practice.
Prioritize an explicit goal and minimal, relevant context; this model is tuned for fast, multi-step coding with tool use, not verbose one-shot essays.
Work iteratively: fire quick attempts, inspect reasoning/output, refine the next turn; the speed and pricing make rapid loops optimal.
Think agentic: break work into steps, define tools or functions, and request structured outputs to chain actions programmatically.
Daily driver for repository edits, refactors, debug loops, and code generation across large contexts at low latency and cost.
IDE and agent workflows where quick multi-step interactions beat one-shot prompting.
Large context window so entire subsystems or docs can be scoped in, but still curate for relevance.
Fast token generation and caching; iterate frequently and reuse stable context to exploit cache savings.
Function-calling/structured outputs and visible reasoning traces for controllable, stepwise work.
Use this layout for each task. It’s concise, deterministic, and built for iterative refinement.
One line stating the goal and target subsystem.
Only the files, functions, config, logs, and constraints truly needed. Use headers/tags to segment.
Explicit acceptance criteria, performance/latency budgets, API contracts, and edge cases.
Ask for a 3–6 step plan first if the task is complex, then “confirm and execute.”
Request exact diffs, file paths, code blocks, or JSON schemas to enable automation.
Boundaries (don’t change X, preserve ABI, tests must pass locally, no third-party deps).
Paste and fill sections; iterate by adjusting Inputs and Requirements between runs.
File: services/db.py
File: services/handlers.py
Env: Python 3.11, uvloop, FastAPI 0.111
Current issue: I/O blocks event loop when heavy queries run.
Replace blocking calls with non-blocking pattern compatible with FastAPI’s event loop.
Maintain existing handler signatures and logging format.
Add retries with jitter and timeouts; ensure p95 latency ≤ 150ms for common queries.
Include unit tests for handlers and wrapper.
Propose a short plan with steps and file edits. Ask “confirm to execute?” Then wait.
Produce unified diffs for files changed.
Provide a final checklist: updated files, commands to run tests, and a brief risk note.
Do not modify config.yml or shared/lib/metrics.py.
No new external dependencies.
Turn 1 (planning request): “Propose a concise plan and impacted files; wait for confirmation.”
Turn 2 (execution): “Apply plan; output unified diffs + test commands; return JSON summary of changes.”
Curate slices: include only functions and call-sites involved; link interfaces through short stubs for clarity.
Use markers like headers or XML-like tags for sections, e.g., , code:services/db.py, , so the model maps constraints to code.
Observe result and reasoning; tighten constraints or add failing test cases; rerun quickly to converge.
Reuse stable context to benefit from prompt caching and keep costs low during many small retries.
Provide failing test output and the minimal snippet that triggers it; request a reproduction test if missing.
Ask for a patch with an explanation of root cause and the invariant it preserves.
Request JSON summaries with fields like files_changed, commands, risks, followups to drive downstream scripts.
For tool use or agents, define callable functions/contracts and require the model to produce function calls with arguments.
Do: be explicit on goals, constraints, file paths, and acceptance tests.
Do: break bigger tasks into confirmable steps, then apply diffs.
Don’t: dump entire repos; select only relevant slices.
Don’t: rely on one-shot prompts for complex changes—loop fast instead.
Model metadata: x-ai/grok-code-fast-1, 256k context, low-cost pricing; suitable for high-frequency iterations.
IDE/agent integrations and previews bias toward inline edits and fast confirm-apply workflows.
Planning: “Read the snippets below and propose a 4-step plan with impacted files; wait for confirm.”
Execution: “Implement the confirmed plan; output diffs and a JSON summary; no unrelated edits.”
Debug: “Given this failing test output and snippet, isolate root cause, propose fix, and produce patch + test.”
Use the structure above with tight scopes and rapid confirm–execute loops to exploit grok-code-fast-1’s strengths: speed, low cost, and large context for practical, agentic coding.