Prompt Engineering: 7 Techniques That Actually Work

The gap between a mediocre AI output and a genuinely useful one rarely comes down to which model you use. ChatGPT, Claude, Gemini — all of them can deliver outstanding results. The difference is almost always in how you write your prompt.

Here are 7 proven prompt engineering techniques used in real business contexts, with concrete examples you can apply right away.

1. Provide context and assign a role

An LLM without context guesses. An LLM with context delivers. The simplest and most effective technique is to frame the conversation upfront: who is speaking, to whom, and in what situation.

Weak prompt:

Write a follow-up email for a client.

Prompt with context:

You are a sales director at a 50-person consulting firm specializing in data.
A prospect (CTO of a manufacturing company) hasn't responded to your
proposal sent 10 days ago.

Write a follow-up email: professional but not pushy, 3-4 sentences max.

The second prompt produces an email you can send as-is. The first produces generic filler that needs a complete rewrite.

2. Use delimiters to structure your input

When your prompt includes data — a text to analyze, a brief, specifications — clearly separate instructions from data. Delimiters remove ambiguity and help the model parse your intent.

Analyze the customer feedback below and extract:
- Satisfaction points (list)
- Pain points (list)
- One priority action recommendation

---
CUSTOMER FEEDBACK:
"""
The tool is fast and the interface is clean, but the PDF export
crashes about a third of the time. Support took 4 days to respond.
We're considering not renewing if this isn't fixed.
"""

Triple quotes, dashes, XML tags — all work fine. What matters is making the structure unambiguous for the model.

3. Specify the output format

Don’t let the model decide how to present its answer. Specify the exact format you need: table, JSON, bullet points, email, paragraph with a word count. This eliminates reformatting work downstream.

From this feature list, generate a comparison table in Markdown
with the following columns:
| Feature | Priority (high/medium/low) | Estimated effort | Business impact |

Features:
- SSO authentication
- CSV report export
- Real-time dashboard
- Slack notifications

This technique is especially valuable in automated workflows where the LLM output feeds into another tool or pipeline.

4. Think step by step (chain-of-thought)

For complex problems — strategic analysis, technical diagnosis, financial evaluation — ask the model to reason through the problem incrementally. This approach, known as chain-of-thought prompting, significantly reduces reasoning errors.

An e-commerce client generates $2M in annual revenue with a 1.2% conversion rate.
They want to invest $50K to improve performance.

Before recommending a strategy, reason step by step:
1. Calculate current visitors and orders
2. Identify improvement levers (traffic, conversion, average order value)
3. Estimate the impact of each lever given the budget
4. Recommend the optimal budget allocation

Without this instruction, the model jumps straight to a recommendation. With it, the model builds a verifiable chain of reasoning you can audit.

5. Show examples (few-shot prompting)

Showing beats telling. If you have a specific format or tone in mind, provide one or two examples. The model will pick up the pattern and replicate it.

Write product descriptions for our AI solutions catalog.

Here's the expected format:

Product: HR Assistant
→ Automate resume screening and candidate pre-qualification.
60% reduction in screening time. Direct integration with your ATS.

Product: Support Chatbot
→ Handle 80% of customer inquiries without human intervention.
Available 24/7, multilingual, deployable in 2 weeks.

Now write in the same format:
Product: Contract Analyzer
Product: Churn Predictor

Few-shot prompting works across all modern LLMs and remains one of the most reliable techniques for controlling style and structure.

6. Add explicit constraints

LLMs tend to produce long, cautious, disclaimer-heavy answers. Constraints channel the response toward what is actually useful.

Suggest 5 names for a startup specializing in AI-powered logistics.

Constraints:
- Maximum 2 words per name
- Easy to pronounce in English
- Plausible .com or .io domain (check naming logic, not DNS)
- No puns on "AI" or "smart"
- Tone: professional, not playful

The more precise your constraints, the less time you spend sorting through irrelevant suggestions. This applies to length, tone, vocabulary, and what to include or exclude.

7. Review and iterate

A prompt is rarely perfect on the first try. The real skill in prompt engineering is iteration. Analyze the response, identify what’s missing, and adjust.

One effective approach is to ask the model to self-evaluate:

Here's the marketing plan you just produced.
Evaluate it against these criteria:
- Consistency with the $20K budget
- Realism of the timeline
- Risks not mentioned

If you identify weaknesses, propose an improved version.

You can also iterate by progressively narrowing: start with a broad request, then tighten with follow-up instructions. This is often more effective than trying to write the perfect prompt on the first attempt.

The bottom line

Prompt engineering is not a dark art. It is a practical skill built on straightforward principles: provide context, structure your request, be explicit about the expected output, and iterate.

Teams that adopt these techniques in their daily workflow — whether on ChatGPT, Claude, or Gemini — see measurable time savings and significantly more usable results.

Next time you get a disappointing answer from an LLM, before switching models, try switching your prompt.