The Tool Desk
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No provider publishes a ranking of these techniques, and none guarantees that a given tactic improves every model or every output. Treat the strategies as hypotheses to measure, not as settings that work automatically.
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What “actually improve” can and cannot promise
Prompt guidance from the major providers is useful but model-specific, and it changes as models change. OpenAI’s prompting guide, Anthropic’s prompting best practices for Claude, and Google’s prompt design strategies for the Gemini API share a common core of clear instructions, explicit outputs, and well-organized context. They do not claim that each tactic produces a measurable gain on every task. Check the page for the model you actually use before relying on a provider-specific detail.
The practical question is therefore not “is this technique good?” but “does this version produce better results on my inputs, judged by criteria I defined in advance?” The rest of this article is organized around that question.
#1 Best Overall
1. Define the task and the success conditions
Most weak prompts fail because they name a topic instead of a job. “Summarize this report” leaves the model to guess the audience, the length, what counts as important, and whether it should rely only on the document. A stronger prompt states the task, the reader, what to include, what to leave out, and what a good result looks like.
Compare these two versions:
- Weak: “Summarize this report.”
- Stronger: “Summarize the report for a nontechnical product manager. Give the three main findings, one limitation, and one next step. Use only the supplied report.”
The second version is an editorial illustration rather than a tested prompt, but it shows the pattern: an audience, an ordered list of required items, and an explicit boundary on sources. If your task has several requirements, list them in the order you want them handled. Ordering matters less to the model than to you, because it makes omissions easy to spot when you review the output.
2. Supply context and separate it from the instructions
A prompt usually mixes three kinds of text: instructions, background material, and the variable input the model should act on, such as a customer email or a block of code. When these run together, the model can mistake a quoted sentence in the context for an instruction, or treat the user’s input as a rule.
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<instructions>
Summarize the report for a nontechnical product manager.
Use only the material inside the report tags.
</instructions>
<report>
[paste report text here]
</report>Use structure when it makes the prompt easier to read and to maintain. A short, single-step prompt does not need tags, and adding them to every prompt is decoration rather than help.
3. Use representative examples for patterns that are hard to describe
Some requirements are easier to show than to state: a tone, a classification scheme, or how to handle an ambiguous case. A few examples can demonstrate these more concretely than a list of rules. Anthropic advises that examples mirror the real use case, vary enough that the model does not lock onto one narrow pattern, and stay clearly marked as examples. Google also treats examples as a core prompt-design element.
Three practical points follow from that guidance:
- Use real inputs. Examples drawn from the actual data you process are more informative than invented ones.
- Vary them. If every example is short and polite, expect short and polite outputs even when the input calls for something else.
- Do not treat one example as proof. An example shows the target; it does not show that the prompt performs well across the range of inputs you will see.
Keep examples separate from the instructions, as described in the previous section, so the model does not confuse a sample answer with the input it should process.
4. Specify the output format
If a person will read the answer, a clear description of the format is often enough: prose, a table, bullet points, or a fixed set of headings. If another program will parse the answer, the format becomes a requirement and needs to be stated exactly.
Useful format constraints include the following:
- Length limits, such as a word count or number of bullet points
- Required headings or field names, written exactly as the downstream system expects them
- Units, date formats, and allowed labels, such as “High, Medium, or Low” with no other values
- What to do when information is missing, such as writing “not stated” instead of guessing
For API work, OpenAI, Anthropic, and Google each document output-format direction, but the mechanisms differ. Check the current structured-output features for the model you have selected, and validate the result in your application. A format request in the prompt is a request, not a guarantee; a schema check or parser in your code is what catches a malformed response.
5. Test revisions against a small evaluation set
This is the strategy that determines whether the other four are working. The approach is simple in principle: keep a set of representative inputs, define what a good answer means before you look at the outputs, and compare prompt versions on the same inputs.
OpenAI documents evaluation methods and graders in its Evals API reference, which is one way to make this process repeatable for API users. You do not need that tooling to start. A spreadsheet with inputs, outputs, and scores is enough for a first pass.
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The OPRO paper from Google DeepMind (2023) describes using a large language model as an optimizer to search for instructions that maximize task accuracy in its experiments. That supports measured optimization as a method. It does not show that automated prompt rewriting will help every task, so the same test discipline applies to any rewrite you generate, whether you wrote it or a model did.
A testing procedure you can follow
- Collect a set of representative inputs. A dozen or so covering typical cases and known edge cases is a practical starting point; this size is a suggestion for manageable review, not a statistically derived number.
- Write down the scoring criteria before running anything. Decide what counts as correct, complete, and compliant with the format.
- Fix the model and its sampling settings for the whole comparison. Changing the model and the prompt at the same time makes the result impossible to attribute.
- Run the current prompt on every input and score the outputs. This is your baseline.
- Change one element, such as adding an example or a format constraint, and rerun the same inputs.
- Keep the change only if it improves the criteria that matter for your task. Record the version, the date, and the scores.
- Rerun the set when the model or provider version changes.
Criteria to score
The table below lists the comparison axes that matter most for prompt versions. These are editorial recommendations for structuring a review, not a standardized benchmark, so define each one in terms your task can support.
| Criterion | What to check | How to score it |
|---|---|---|
| Task accuracy | Whether the answer is correct against a reference answer or a checked fact | Pass or fail per input, then a percentage across the set |
| Completeness | Whether every required item is present | Count of required items present, out of the total required |
| Relevance | Whether the answer stays on the task and on the supplied material | A 1 to 5 rubric written in advance, scored by two reviewers where possible |
| Format compliance | Whether the output matches the requested structure | Pass or fail, checked by a parser or a short checklist |
| Edge-case robustness | Behavior on unusual, ambiguous, or incomplete inputs | Pass rate on a separate edge-case subset |
| Cost and latency | Length of prompt and response, and response time, if used in production | Token counts and timings from your API usage logs |
When the scores do not move
- The criteria are vague. If two reviewers cannot agree on whether an answer is “relevant,” rewrite the rubric before testing again.
- One run looks better than another. Model outputs vary between runs. Compare prompts on the same inputs, and repeat runs when a difference is small.
- An example dominates the output. If responses start copying the example’s wording or length, add more varied examples or remove the example.
- Format breaks after an update. A prompt that passed last quarter can fail after a model change. Rerun the format checks before assuming the prompt is at fault.
- The longer prompt scores worse. Remove the least useful instructions one at a time and keep only the lines that change the scores.
Putting the five strategies together
A practical workflow starts with the task definition, adds only the context and examples the model needs, specifies the output format, and then tests the result on your inputs. Each step is cheap to try and easy to undo, which is why the testing step matters most. Without it, a prompt that reads well can still produce worse results for the cases you care about.
Frequently Asked Questions
Does a longer prompt produce better output?
Not reliably. Added instructions can help when they resolve a real ambiguity, but they can also conflict with each other or dilute the main task. Remove instructions one at a time and keep only those that change the scores on your test inputs.
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Can I reuse the same prompt across ChatGPT, Claude, and Gemini?
The overall structure often transfers, since all three providers emphasize clear tasks, explicit outputs, and organized context. Provider guidance differs on details such as preferred formatting and structured-output features, so run the same evaluation set on each model and adjust the prompt where the scores differ.
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