DeepSeek Large Language Models

DeepSeek Prompts: 6 Built for Volume, Not for One-Off Reasoning

DeepSeek is an order of magnitude cheaper than anything else at volume, which makes it the right tool for high-throughput work where per-token cost dominates rather than for one-off reasoning. These six are built for batch jobs. One thing to plan for: deepseek-chat and deepseek-reasoner are deprecated from 24 October 2026, so anything you build now needs a migration path.

⌨️ 6 prompts 🕐 Updated Aug 28, 2026

💡 How to use these prompts: Replace everything in [BRACKETS] with your specific details before sending. Click Copy to copy any prompt to your clipboard instantly.

Classify at volume with a refusal option

The none option is what keeps a batch job trustworthy. Without it every input gets a label whether or not one fits.

Classify the input into exactly one of these categories: [LIST]

Return only a JSON object with keys "category" and "confidence" where confidence is high, medium or low.

If the input does not fit any category, return "category": "none". Do not force a fit.
If the input is ambiguous between two categories, return the more specific one and set confidence to low.

No commentary before or after the JSON.

Input:
[PASTE]

Extract fields without guessing

Never infer is the important line. At volume, one hallucinated field becomes thousands.

Extract these fields from the text below: [FIELD LIST]

Return a JSON object only. Rules:
- Use null for anything not present. Never infer a plausible value
- Copy quoted fields verbatim, do not paraphrase or normalise
- If the text states something twice with different values, include both in an array and add a "conflict" key

Text:
[PASTE]

Deduplicate a messy list

The uncertain bucket is what makes this safe to run unattended over large datasets.

Below is a list where some entries refer to the same thing with different wording.

Group them. For each group return: the canonical form, every variant that maps to it, and your confidence.

Do not merge entries you are unsure about — put those in a separate "uncertain" group for me to review.

List:
[PASTE]

Summarise to a fixed shape

Fixed shape output is what makes a batch of summaries machine-readable rather than prose you still have to read.

Summarise the text below into exactly this structure:

WHAT: one sentence
WHO: names and organisations, comma separated, or none
WHEN: dates mentioned, or none
NUMBERS: any figures with their units, or none
UNCERTAIN: anything the text implies but does not state

Do not add sections. Do not exceed one sentence in WHAT.

Text:
[PASTE]

Route by intent before spending on a better model

Cheap triage. Route SIMPLE here and send only COMPLEX to an expensive model, which is where the cost saving actually lives.

Read the request below and decide which handler it needs:

SIMPLE — factual lookup or short answer
COMPLEX — multi-step reasoning or judgement required
UNCLEAR — needs clarification from the user before anything else

Return only the label and one short line of reasoning.

Check a batch for drift

Confidently wrong is the failure mode that survives review at volume. This prompt is for catching it.

Below are outputs from the same prompt run across many inputs.

Tell me:
- Whether the format stayed consistent
- Where the model deviated from the instructions
- Which inputs produced the most unusual outputs
- Whether any output looks confidently wrong rather than obviously wrong

Outputs:
[PASTE]