> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Prompt Engineering for Text Summarization
> Advice on few-shot prompt engineering using multiple diverse examples and clear instructions to produce consistent, concise text summaries, with best practices, examples, and implementation tips.
Question 10.
A developer needs to implement a text summarization feature using an LLM.
Which prompt engineering strategy would be most effective?
* Providing a single example of a good summary.
* Explicitly instructing the model to be concise and extractive.
* Using chain-of-thought prompting with reasoning steps.
* Including multiple examples of different summary styles with clear instructions.
Answer: including multiple examples of different summary styles with clear instructions.
Few-shot prompting—providing multiple, diverse examples with explicit instructions—is the most effective approach for reliable text summarization. Examples demonstrate the expected format, tone, length, and level of abstraction (extractive vs. abstractive), helping the model produce consistent and predictable summaries.
Why this is the best choice
* Few-shot prompting demonstrates the target output
* Examples show concrete structure (bullet lists, one-paragraph summary, headline) and typical length.
* Multiple styles (concise, technical, lay) teach the model how to vary outputs based on instruction.
* Explicit instructions alone are useful but often insufficient
* Directives like “be concise” or “extract the main points” steer the model, but examples make expectations concrete and measurable.
* One example can be limiting
* A single example may overfit the model to that single format. Multiple examples reveal acceptable variability and reduce ambiguous behavior.
* Chain-of-thought is usually inappropriate for concise summaries
* Chain-of-thought elicits intermediate reasoning steps that can produce verbose outputs; summarization typically requires a clean final output without revealed internal reasoning.
Comparison of prompt strategies
| Strategy | Use Case | Recommendation |
| -------------------------------- | ------------------------------: | --------------------------------------------------------------- |
| Single example | Quick demonstration of format | Useful for very constrained tasks, but may overfit the style. |
| Explicit instruction | Direct control over length/tone | Always include, but pair with examples for clarity. |
| Chain-of-thought | Complex multi-step reasoning | Not recommended for concise summarization — leads to verbosity. |
| Multiple examples + instructions | Robust, flexible summarization | Best practice: few-shot with diverse examples and clear labels. |
Recommended prompt pattern (high level)
1. Start with a concise instruction that states format, length, tone, and whether the summary should be extractive or abstractive.
2. Provide 2–4 labeled examples that follow the instruction. Ensure examples vary by source text and style so the model learns acceptable variations.
3. Present the new input and include a label such as `Summary:` for the model to complete.
4. Optionally include explicit evaluation criteria (e.g., max word count, must include 2–3 key facts) to improve consistency.
Example prompt sketch
```text theme={null}
Instruction: Summarize the input in one paragraph (~30–50 words). Use a neutral tone and focus on the main facts.
Example 1
Input: The quarterly report showed 12% revenue growth driven by overseas sales. Operating costs rose slightly due to supply-chain issues.
Summary: Revenue grew 12% this quarter, led by international sales, while operating costs increased modestly because of supply-chain disruptions.
Example 2
Input: The study found that daily exercise improved mood and cognitive performance across all age groups, especially in older adults.
Summary: Regular exercise boosts mood and cognitive performance for all ages, with particularly strong benefits for older adults.
Now summarize:
Input:
Summary:
```
Best practices and tips
* Use clear labels (e.g., `Input:`, `Summary:`) to make the expected completion explicit.
* Vary example lengths and tones to teach the model how to adapt when you request a specific style.
* If you need extractive summaries, include examples that highlight exact phrases from the input.
* For abstractive summaries, include examples that condense or rephrase content while preserving meaning.
* Test with edge cases (long inputs, ambiguous language) and add targeted examples if the model struggles.
Avoid chain-of-thought prompting for production summarization where concise outputs are required: it can produce long, unnecessary internal reasoning and leak sensitive information. Use chain-of-thought only when you explicitly need the model to show intermediate steps for debugging or analysis.
Links and references
* [Prompt engineering overview — best practices and patterns](https://arxiv.org/abs/2107.07502)
* [Few-shot learning for LLMs — techniques and examples](https://platform.openai.com/docs/guides/few-shot)
* [Kubernetes Basics](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/) (example external reference)
Implementing this in your workflow
* Start with a curated set of 10–30 diverse examples covering the common cases your users will submit.
* Iterate: collect failed summaries, add them as new examples, and refine instructions.
* Consider automated evaluation: measure ROUGE/BLEU for extractive vs. abstractive targets or use human-in-the-loop feedback for quality control.