Design a prompt to generate a summary of a technical article on renewable energy. What considerations would you take into account to ensure accuracy and completeness?

Give the model the full article, say who the summary is for and what it must cover, require that every statement comes from the article, and fix the length and structure. Then check the summaries against the source.

The prompt

You are summarising a technical article for energy-sector analysts who read
many papers and need the facts quickly.

<article>
{full article text}
</article>

Write a summary of the article above.

Requirements:
- 200 to 250 words, in four short sections: Main finding, Method, Key figures, Limitations.
- Use only information stated in the article. Do not add background knowledge,
  forecasts or opinions.
- Keep every figure exactly as written, with its unit and what it measures
  (for example "18.5% cell efficiency in laboratory tests").
- Use the article's technical terms, briefly explaining any that a
  non-specialist analyst might not know.
- If the article does not state its limitations, write "Not stated".
- After the summary, list each figure you used with a short quote from the
  article that supports it.

Considerations for accuracy

• Source only: summarisation errors usually come from the model adding plausible facts it knows about the topic. The instruction to use only the article, plus quotes for figures, keeps it honest and makes checking quick.
• Numbers and units: renewable energy articles are full of capacities (MW vs MWh), efficiencies and costs. Ask for exact figures with units, and check them; this is where summaries most often go wrong.
• Hedging preserved: "may reduce costs in pilot sites" must not become "reduces costs". Ask the model to keep the article's level of certainty.
• Verification: for important use, a second step can compare each sentence of the summary with the article and flag anything unsupported.

Considerations for completeness

• Fixed sections ensure the method and limitations aren't dropped in favour of the headline result.
• Placement: put the long article before the instructions. Models make better use of long inputs when the question comes after them, and they can miss details buried in the middle of very long texts. For very long articles, summarise section by section, then combine.
• Audience: the same article needs a different summary for analysts, executives or the public. Saying who reads it decides the detail and vocabulary.

Testing the prompt Run it on several articles with different structures (a study, a policy report, a product announcement), and compare the output with a human-written summary or a checklist of key points. Check every figure. Adjust the sections or length if important points are regularly missed.

If the summaries feed a database or a newsletter, ask for JSON with one field per section instead of prose, so missing sections are caught by code before anyone reads the result.