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Better Moonshot prompts are specific, structured, and testable. State the task and audience, provide the relevant context, set constraints, define the exact output format, and include a representative example. For difficult or underspecified work, tell the model to list missing information before it starts. This approach follows Moonshot’s documented guidance that clear instructions plus representative examples produce more stable outputs.
The model you choose also changes what a good prompt can accomplish. K2.6 is positioned for fast conversation and question answering, K3 for advanced general chat and agent work, and K3 Swarm for parallel search or batch processing. Long documents, files, images, tools, and API workflows each need explicit prompt instructions rather than a generic request.
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What should a Moonshot prompt contain?
A reliable prompt normally has six parts, in this order: perspective, task, audience and background, constraints, output schema, and an example. You do not need every part for a one-line question, but adding the relevant parts removes ambiguity and makes results easier to evaluate.
- Role or perspective: Tell the model what kind of specialist or viewpoint is useful, such as a compliance editor, Python reviewer, or financial analyst. Use a role only when it changes the decisions the model should make; a decorative title adds little.
- Concrete task and outcome: Use an observable verb such as extract, classify, compare, rewrite, calculate, or generate. Define what a successful answer must contain.
- Audience and background: Identify who will use the result and supply definitions, business context, assumptions, and relevant facts. Do not make the model infer terms that have a specific meaning in your project.
- Constraints: Specify date range, geography, edition, length, tone, exclusions, safety rules, source requirements, and what to do when evidence is missing.
- Output schema: Name the headings, fields, table columns, ordering, units, and serialization format. If software will consume the response, provide a JSON schema or another machine-readable contract.
- Representative example: Show an input and an ideal output, including an edge case when one is likely. Moonshot says examples are one of the most effective ways to stabilize output.
End the prompt with a quality check. Ask the model to verify required fields, flag unsupported claims, and report unresolved ambiguity instead of silently guessing.
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How do I turn a vague request into a precise task?
Rewrite the request as a small specification before adding prose. A useful specification answers what to do, with which material, for whom, under what limits, and in what form.
Start with the decision or deliverable
“Tell me about this report” leaves the success condition undefined. “Extract every control failure from the report and return a table with control ID, evidence, risk, owner, and page number” gives the model a finite operation and a testable result.
Define the audience and use
The same facts need different treatment for an engineer, a board member, or a customer. State the reader’s knowledge level and the action they will take. For example, ask for a decision memo for a procurement committee rather than a general summary if the output will support a purchase decision.
State what the model must not do
Negative constraints prevent common failure modes. Say whether the model may infer missing values, rewrite quoted text, use information outside the supplied files, or include recommendations. If evidence is incomplete, require a label such as “not established” rather than an invented answer.
Ask for clarification when the task is underspecified
Include an instruction such as: “Before proceeding, list any missing facts that would materially change the answer. Ask no more than five questions; if none are material, continue.” This keeps a polished but unjustified answer from hiding an ambiguous requirement.
How can I make Kimi follow a format reliably?
Describe the structure twice: once in plain language and once as a compact schema or example. Tell the model what each field means, whether it is required, the allowed values, and what to return when a value is unavailable.
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For a report, specify the exact heading names and order. For a comparison, name every column and the unit for numeric values. For a checklist, require one item per line and a status value such as pass, fail, or unknown.
Use a machine-readable contract for software
When an application parses the response, request valid JSON only and define required keys, data types, enumerated values, and null behavior. Instruct the model not to add Markdown fences or commentary outside the object. Validate the response in your application and return a targeted repair prompt if a field is missing or has the wrong type.
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Show an edge-case example
An example should demonstrate the difficult part, not merely repeat the easiest case. Include what happens when two sources disagree, a field is absent, an image is unreadable, or a record matches more than one category. The example becomes an executable interpretation of your words.
How should examples and quality checks work together?
An example teaches style and boundary decisions; a quality check tests whether the generated answer obeyed them. Keep the example short enough that it does not consume the context needed for the real material, but detailed enough to show the expected granularity.
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Specify evidence behavior
Tell the model how to distinguish supplied facts from inference. A strong instruction is: “Use only the attached material for factual claims. Mark a conclusion as an inference and quote the supporting passage or page.” If no source supports a requested value, require “not stated” and identify the missing evidence.
Separate planning from the final format
For multi-step work, ask the model to perform an internal checklist but return only the requested schema. If you need an audit trail, add an explicit field such as assumptions or validation_notes rather than accepting unstructured reasoning around the answer.
Make evaluation objective
Define checks that another program or person can apply: all records included, totals reconcile, dates use ISO format, no prohibited terms appear, and every recommendation cites an input fact. Objective checks make prompt changes measurable.
How do context windows affect long Moonshot prompts?
Input and output share the selected model’s context budget. A long instruction, many examples, and large attachments leave fewer tokens for the answer, so reserve output space instead of filling the window with background that does not affect the decision.
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|---|---|---|
| Kimi K3 | Moonshot describes a 1-million-token context in current company materials. | Suitable for very large document sets, but still label files and define the operation so the model can locate relevant material. |
| Moonshot V1 options | The current API model list documents 8K, 32K, and 128K context options. | Select the specific context variant deliberately; do not assume every V1 endpoint has the largest window. |
| Kimi K2.5 | An AWS 2026 model card lists a 256K-token context and a 16K maximum output. | Leave room for the requested response and verify the limits of the endpoint you actually call. |
These figures are model-specific and can change. Check the current model documentation before deployment, especially when a model alias may point to a different revision.
Budget the window before sending
Estimate the size of instructions, examples, conversation history, file text, and desired output. Remove duplicated background, summarize earlier turns, and request a bounded answer when a complete dump is unnecessary. For a large corpus, ask for extraction or indexing first, then run focused prompts over the relevant portions.
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Label long inputs for retrieval
Give every attachment a stable name and, when possible, include section titles, page numbers, or row ranges. Ask the model to cite those labels in its output. This reduces confusion when several files contain similar terminology.
Which Moonshot model should I use?
Choose by workload rather than by name alone. The official Kimi overview positions the current options differently, while the exact limits and lifecycle status belong to the model documentation for the endpoint you plan to use.
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| Fast conversation and routine Q&A | K2.6 | Keep the instruction compact, specify the answer format, and avoid spending tokens on elaborate background for simple questions. |
| General chat, coding, analysis, spreadsheets, slides, and agent work | K3; presented as natively multimodal | Describe the artifact and the action required, then add validation criteria for calculations or code. |
| Large-scale parallel search or batch processing | K3 Swarm | Define the unit of work, a stable output schema, failure handling, and how results from parallel jobs should be merged. |
| Applications using Moonshot V1 endpoints | 8K, 32K, and 128K context choices are listed | Match the prompt and output budget to the selected context variant; do not transfer assumptions from another model. |
| Deployments using Kimi K2.5 | AWS lists 256K context and 16K maximum output in its 2026 model card | Use the documented endpoint limits and confirm availability in your region and account. |
Kimi’s product overview also says web search may be selected automatically. Treat that as a capability that can be chosen by the product, not as a guarantee that every request will browse or that every API call has internet access.
How do I prompt Kimi with PDFs, spreadsheets, images, and video?
Name each attachment and state the operation to perform on it. Kimi’s current help documentation lists PDF, Word, Excel, PowerPoint, image, and video handling, with files up to 100 MB each and up to 50 files in a session.
- Assign a stable filename: Refer to files as
budget_2026.xlsxorcontract_v3.pdf, not “the first file.” - Declare the operation: Use verbs such as “extract the table,” “compare these two images,” “summarize the PDF for a compliance team,” or “turn this spreadsheet into a decision memo.”
- Specify scope: Identify sheet names, page ranges, image regions, timestamps, or columns. Say whether hidden rows, footnotes, speaker notes, and appendices count.
- Define the output: Request the exact fields, units, ordering, and citations to filename and page, sheet, or timestamp.
- Handle unreadable material: Require the model to report an unreadable page, low-resolution image, or missing sheet instead of reconstructing it silently.
For comparisons, describe the relationship between files: “Compare the signed contract with the redline and list every changed obligation.” For transformations, state whether the source must remain verbatim and where interpretation is allowed.
Can a prompt make Moonshot browse the web or call my database?
No. A prompt alone does not grant internet, database, or application access. Moonshot’s API documentation says models do not directly access external resources by default; you must configure official tools or custom function calls.
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Describe each tool as a contract
For every function, define its purpose, input fields and types, permitted values, authentication boundary, timeout or failure behavior, and response fields. Tell the model when the tool is appropriate and when it must not be used. Include a short example call for ambiguous arguments.
Use the tool-calling loop
- Send the user request together with the available function definitions.
- Allow the model to decide whether a function is needed and return a structured call.
- Validate the function name and arguments in your application before execution.
- Run the permitted operation and send the structured result back to the model.
- Ask for the final response in the original output schema, including a clear error when the tool failed.
The Kimi-K2 README documents this pattern: pass available function tools with each request and let the model decide when and how to invoke them. Keep permissions in the application layer; do not rely on wording in a prompt as an access-control mechanism.
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What generation settings should I use?
Settings are model-specific. The MoonshotAI Kimi-K2 README recommends temperature = 0.6 for Kimi-K2-Instruct; that recommendation should not be treated as a universal value for every current Kimi model.
For repeatable extraction, keep the prompt, model revision, tool definitions, and temperature fixed while you evaluate changes. For creative drafting, a different temperature may be appropriate, but record it with the prompt version so results remain comparable. Confirm supported parameters and defaults in the API documentation for the selected model rather than copying settings from another family.
What is a reusable Moonshot prompt template?
Adapt the following template to your task. Replace the bracketed values inside the code block; they are variables, not text to send unchanged.
Task: [specific outcome and success condition]
Audience: [who will use the answer and what they know]
Context: [facts, files, definitions, and assumptions]
Constraints: [length, date, geography, exclusions, safety and evidence rules]
Process: [steps to follow, including when to use configured tools]
Output: [exact headings, fields, table columns, or JSON schema]
Quality check: [required checks and what to do when information is missing]
Example: [representative input and ideal output, including an edge case]If the task involves tools, add each function’s contract after the process section. If it involves files, list filenames and the operation for each one before the output section.
What do strong prompts look like in practice?
Long-document compliance review
Weak: “Review these contracts and tell me what matters.”
Stronger: “Act as a contract compliance analyst. Review master_agreement.pdf and vendor_appendix.pdf for obligations due before 31 December 2026. Return a table with document, page, clause, obligation, due date, responsible party, evidence, and confidence. Use only the files, mark missing dates as ‘not stated,’ and list any clause that conflicts across documents. Before writing the table, identify questions whose answers would change the scope.”
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The second version defines the corpus, time boundary, audience role, evidence rule, conflict handling, and output fields. An example row showing a conflicting clause would further stabilize the interpretation.
Image comparison
Prompt: “Compare label_front.png and label_revision.png. Identify every visible text, color, logo, and layout change. Report one change per row with region, old value, new value, and whether the change could affect regulatory labeling. If a region cannot be read, report it as unreadable rather than guessing.”
Spreadsheet decision memo
Prompt: “Use sales_q1.xlsx, sheet Regional totals. Recalculate the totals from rows 4–220, flag any mismatch with the provided total, and write a 500-word decision memo for the operations director. Include a table of region, computed total, stated total, variance, and status. Use the workbook’s currency and date conventions; do not create forecasts.”
Coding with a tool
Prompt: “You are a Python reviewer. Inspect the attached module and propose the smallest change that fixes the failing test. You may call run_tests with a test path and timeout; do not call other tools. Return JSON with diagnosis, patch, tests_run, and remaining_risks. If the test tool fails, report its error and do not claim the patch passed.”
Parallel batch classification
Prompt: “For each record, assign exactly one category from billing, technical, account, or other. Return the record ID, category, evidence phrase, and confidence. Process records independently, preserve input order in the merged result, and mark ambiguous records for review. Do not invent an ID or category.”
How do I troubleshoot inconsistent Kimi outputs?
- The answer is too broad: Replace general verbs such as “discuss” with a bounded operation, add a time or geographic scope, and define the required fields.
- The format drifts: Add a schema, allowed values, one edge-case example, and an instruction to return no commentary outside the schema.
- Facts are invented: Restrict the evidence source, require “not stated” for absent values, and request source locations in the output.
- Important files are ignored: Name every attachment, state the operation for each, and identify pages, sheets, columns, or timestamps.
- The response is truncated: Shorten repeated context, reduce example size, lower the requested output length, or choose a model and endpoint with a larger documented context.
- Tool calls fail: Validate arguments before execution, describe permitted values and failure responses, and return the tool error to the model in a structured form.
- Results change between runs: Pin the model revision when possible, hold settings and tool definitions constant, and evaluate against a fixed test set.
- A model behaves differently after an update: Recheck the current model list, context limits, capabilities, and deprecation notices before changing the prompt.
How should I maintain prompts as Moonshot models change?
Keep prompts in version control with the model identifier, endpoint, generation settings, tool definitions, file limits, and a small regression set. Run that set whenever you change a prompt, model, temperature, schema, or tool contract.
Model names, capabilities, and deprecations change. Moonshot’s current documentation should be the authority for availability and limits at the time you publish or deploy. Treat the 1-million-token K3 description, Moonshot V1 context variants, and K2.5 figures as dated, model-specific information rather than permanent platform guarantees.
A useful release record contains the prompt version, representative inputs, expected fields, acceptable variations, failure examples, and the date and region in which the endpoint was checked. This makes a capability change visible before it reaches users.
Frequently Asked Questions
Can I reuse one prompt across different Moonshot models?
You can reuse the task specification and examples, but retest context budgeting, multimodal behavior, tool support, and output quality for each model. Keep model-specific settings and limits outside the shared prompt so they can be changed without rewriting the task definition.
What should I do when my files exceed the session limits?
Split the material into logically named batches, process each batch with the same schema, and run a final merge prompt over the extracted results. Preserve page, sheet, or timestamp references so the merged answer remains traceable to the source.
How can I compare two models fairly?
Send the same frozen inputs, prompt version, tool definitions, and evaluation rubric to both models. Compare required-field completion, factual support, error handling, latency, and total token usage rather than judging a single attractive response.
Does Kimi’s automatic web search run on every question?
No. The product overview says web search may be selected automatically, which is different from a guarantee that every request will browse. If current external information is essential, configure an approved retrieval tool and require the answer to identify the returned sources.
What belongs in a prompt regression test set?
Include normal cases, ambiguous requests, missing fields, conflicting sources, unreadable files, invalid tool arguments, and the largest realistic input. Store the expected schema and acceptable alternatives, not just one exact wording, so useful variation is not mistaken for failure.
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