You tell your AI to classify customer support tickets. It gets it wrong. So you add an example. Better. You add more examples. Now the prompt is 4,000 tokens and the AI still misses edge cases. Adding more examples makes it worse, not better.
The problem isn't the number of examples - it's which examples you show. A single well-chosen example often outperforms twenty poorly chosen ones. But how do you choose the right examples for each unique situation?
That's where few-shot example management comes in. Curate once. Select dynamically. Get consistent results.
The best examples for any task aren't fixed - they're selected at runtime based on what the AI needs to understand. A curated library of examples combined with semantic retrieval beats static examples every time.
PROMPT ENGINEERING PATTERN - The systematic approach to showing AI how to respond. Instead of telling the AI what to do, show it examples of what you want. But show the right examples.
Few-shot example management is the practice of curating, organizing, and dynamically selecting examples to include in prompts. Instead of hardcoding a fixed set of examples, you maintain a library of high-quality examples and select the most relevant ones for each specific request. The AI learns the pattern from the examples rather than from explicit instructions.
Think of it like training a new employee. You could give them a 50-page manual, or you could show them three well-chosen examples of the exact type of work they'll be doing. The examples communicate format, tone, edge cases, and expectations in a way that instructions often can't. But you wouldn't show them the same three examples for every task - you'd pick examples relevant to what they're working on.
Few-shot learning is one of the most powerful techniques for getting consistent AI behavior. The challenge isn't whether examples help - they clearly do. The challenge is managing examples at scale: which to include, how many, and when to update them as your needs evolve.
Few-shot example management solves a universal problem: how do you communicate expected behavior to an AI in a way that scales across thousands of variations without bloating every prompt?
Build a library of high-quality input/output pairs. Tag each example with metadata (category, difficulty, edge case type). When processing a request, embed the input and retrieve the most semantically similar examples from your library. Insert them into the prompt. The AI extrapolates from what it sees.
Choose a customer input type, then watch as the system finds the most relevant examples from the library.
“I received my order but the item is broken. Can I send it back?”
Input: I want to return a damaged product I received yesterday
Input: Can I return something I bought 3 months ago?
Input: My package says delivered but I never got it
Input: How do I track my order?
Input: I was charged twice for my order
Input: What payment methods do you accept?
Find examples similar to the current input
Embed your input, search your example library by vector similarity, return the top-k matches. If someone asks about 'return policy for damaged items,' you retrieve examples about returns and damage - not your most generic examples. This is the gold standard for dynamic selection.
Pre-classify inputs, then select from matching category
First classify the input (intent, topic, complexity). Then pull examples tagged with that classification. Customer asks about billing? Show billing examples. Asks about technical issue? Show technical examples. Simpler than semantic search, but still context-aware.
Cover the range of possibilities
Instead of most-similar, select examples that span the diversity of your output space. Include one short response, one long response. One formal, one casual. One straightforward case, one edge case. This teaches the AI the full range of acceptable outputs.
A customer asks about refund eligibility. The system embeds their question, searches the example library for similar past interactions rated 5 stars, and injects those as few-shot examples. The AI generates a response that matches the proven successful patterns.
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You show one example where the AI says 'I apologize for the inconvenience' and another where it says 'We don't apologize for user error.' The AI receives mixed signals. It might blend both approaches awkwardly, or oscillate between them unpredictably. Consistency suffers.
Instead: Curate examples that demonstrate a single, coherent policy. When you have conflicting approaches for different scenarios, use metadata to ensure only consistent examples appear together.
You figure more examples = better learning, so you stuff 15 examples into every prompt. But now you've consumed most of your token budget before the actual request. Worse, irrelevant examples can confuse the model about what's important. Response quality drops.
Instead: Test with 1-3 examples first. Add more only if quality improves. Often 2-3 well-chosen examples outperform 10 mediocre ones. Use semantic retrieval to ensure every example earns its tokens.
Your examples are from six months ago. Since then, your product changed, your tone guidelines evolved, and you handle certain cases differently. But your AI keeps producing outdated patterns because that's what the examples show. Users notice the inconsistency.
Instead: Treat examples like code - version them, review them, update them. When policies change, update corresponding examples. Run periodic audits. Flag examples that no longer reflect current best practices.
You've learned how to curate and dynamically select examples for consistent AI behavior. The natural next step is understanding how to structure your system prompts to incorporate these examples effectively.