By skyforbes This guide synthesizes empirically-grounded best practices.
1. Few-Shot Prompting:
Theoretical Foundation:
Few-shot prompting leverages in-context learning, where demonstrations within the prompt establish task-specific patterns without requiring parameter updates. Brown et al. (2020) demonstrated that 3-5 carefully selected examples can improve performance on complex tasks by 20-40% compared to zero-shot approaches. The mechanism operates through pattern recognition activation, where examples activate relevant pathways in the model's parameter space.
Think of this as teaching by demonstration—just as humans learn more effectively when shown concrete examples, AI models similarly benefit from seeing the pattern you want them to follow.
How to implement:
- Pick Quality Over Quantity:
-Usually 3-5 examples hit the sweet spot. For really complex stuff, you might need 8-12, but don't go overboard.
- Choose Smart Examples:
-Mix it up—show different types of the same problem
-Cover the tricky edge cases
-If you're categorizing things, show equal examples from each category
- Make It Easy to Follow:
-Keep all your examples in the same format
-Start simple, then get more complex
-Use clear labels like "## EXAMPLE 1:" so the AI knows where one ends and another begins
2. Few-Shot prompting for Classification tasks:
Theoretical Foundation:
Class distribution in demonstrations influences decision boundary learning. Sequential presentation of same-class examples (A, A, A, B, B, B) creates temporal bias, where recent examples disproportionately influence predictions through recency effects. Mixed presentation (A, B, A, B) encourages development of discriminative features rather than sequential pattern matching.
Implementation Strategies:
- Shuffle Your Examples:
Instead of: A, A, A, B, B, B, C, C, C
Try: A, B, C, A, B, C, A, B, C
- Smart Mixing Strategies:
Put similar-but-different examples next to each other
Include both easy and tricky examples for each category
+Make sure each category gets equal representation
Tips for few shot prompting for classification tasks
Try different orderings of the same examples. See if mixing improves accuracy.
3. Keep it simple (Minimal sufficiency principle)
Theoretical Foundation:
The principle of minimal sufficiency suggests that prompts should contain only the information necessary for task completion. Complexity introduces cognitive interference and interpretation variance, increasing the model's tendency toward overgeneralization or pattern mismatch. Simpler prompts reduce the solution space and minimize ambiguity.
How to implement:
- Write Like You're Giving Direct Orders
Say "Classify this document" instead of "This document should be classified"
- Organize Your Thoughts:
Say what you want first, add details secon, and cut anything you're repeating (unless you're doing it on purpose for emphasis)
- Use Action Words:
Words like "Analyze," "Categorize," "Extract," and "Synthesize" work 15-30% better than vague descriptions.
Tips for keeping it simple:
Try the same task with a simple prompt and a complicated one. See which gives you better, more consistent results.
4. Be specific about the output:
Theoretical Foundation:
Specificity reduces solution space entropy, allowing the model to allocate computational resources more efficiently. The precision-recall trade-off in prompt engineering suggests that overly broad prompts maximize recall of possible answers but minimize precision of desired answers. Explicit constraints guide the model toward the target output distribution.
How to implement:
- Spell Out the Details:
-Instead of "Give me recommendations," say "Give me exactly 3 recommendations"
-Instead of "Write naturally," say "Write in a conversational style"
-Instead of "List them," say "Give me a bulleted list"
- Multi-Modal Output Specification:
-When models support multiple output types, explicitly define:
· Format: "Return JSON with keys X, Y, Z"
· Schema: Include type specifications (String, Number, Array)
· Constraints: "Dates must be in ISO 8601 format"
· Exemplar Inclusion: When possible, include a partial or complete output example like:
Return data like this:
{
"analysis": [{"topic": "climate change", "confidence": 0.95}],
"summary": "Brief overview here"
}
Tips about being specific:
When possible, include a partial or complete output example. This serves as both specification and demonstration, reducing ambiguity about expectations.
Note: Try to use XML unless you specifically need JSON– it’s simply cheaper and stricter.
5. Use Instructions Over Constraints
Theoretical Foundation:
This practice aligns with positive framing theory in human-computer interaction and solution-focused prompting in LLM research. While constraints define boundaries of exclusion, instructions establish pathways of inclusion. The negation processing limitation in transformer architectures suggests models process negative instructions ("don't do X") less efficiently than positive ones ("do Y").
Instructions provide constructive guidance, whereas constraints only mark boundaries without indicating the preferred path.
How to implement:
- Flip Your "Don'ts" into "Do's":
-Instead of "Don't include personal opinions" → "Base responses only on facts from the provided sources"
-Instead of "Avoid technical jargon" → "Use language a high school student would understand"
- Save "Don'ts" for Important Stuff:
-Safety: "Do not generate harmful content"
-Hard limits: "Do not exceed 500 words"
-Legal stuff: "Do not include personal information"
6. Control the Maximum Tokens
Theoretical Foundation:
Token limitation represents a computational budget allocation problem. Unlimited generation allows topic drift and redundancy accumulation. Research indicates an optimal response length exists for most tasks, beyond which quality plateaus or deteriorates due to dilution of relevant information.
How to implement:
- Double Up on Controls:
-In your prompt: "Explain in about 300 words"
-In your settings: Set max_tokens = 500
- Match Length to Task:
-Summaries: About 10-20% of the original length
-Code: Allow 50% extra tokens beyond estimated need to accommodate comments and error handling
-Creative writing: Higher limits with temperature control to balance creativity and coherence
Tips on controlling max tokens:
-Track quality versus length. You'll find that point where more words don't mean better answers.
-Stop when quality stops improving
7. Experiment with Input Formats and Writing Styles
Theoretical Foundation:
Different formulations activate distinct latent representations within the model. The formulation variance hypothesis suggests that semantically equivalent prompts with different syntactic structures can yield statistically different outputs due to the model's training distribution biases. This occurs because models are sensitive to both semantic content and surface-level linguistic features.
How to implement:
- Mix Up Your Approach:
Question: "What are the implications of X?"
Command: "Describe the implications of X"
Statement: "The implications of X include…"
Scenario: "Imagine explaining X's implications…"
- Vary Your Style:
Casual → Professional → Academic
First-person vs. third-person
Past, present, or future tense
8. Prompt templates:
Templates are a way to create reusable prompts
Example:
As a {expert_role}, analyze this {document_type} about {topic}.
Focus on {analysis_aspects}.
Provide {output_format} suitable for {target_audience}.
"""
9. Meta-Prompting:
Consider using the model itself to optimize prompts:
"Rewrite this prompt/ improve this prompt"
10. Reverse prompting:
"What prompt might have produced this?
11. Clarify-first prompting:
"Ask me 3 questions to better help you"
"Before answering, list what you need to know"