Many-Shot In-Context Learning: The Evolution of Few-Shot in the Long-Context Era

TL;DR — For years we'd put one or two examples in the prompt and tell the model "do it like this"; we called this few-shot learning. With the explosion of long context windows, we can now fit dozens, hundreds, even thousands of examples into the prompt — this is called many-shot in-context learning. And it's a game-changer: on many tasks, without fine-tuning, just by increasing the number of examples, you can get results close to fine-tuning. In this piece I explain, from the field, what the shift from few-shot to many-shot brings, when to prefer it over fine-tuning, how to manage the cost with prompt caching, the subtleties of example selection and ordering, and practices in Turkish tasks.

One of the classic tools of prompt engineering was few-shot learning. The idea is simple: instead of describing the task to the model, you show it a few examples. Three to five examples in the form "here's the input, here's the output I want" help the model grasp what it should do. This worked well for years and still does.

But few-shot had a limit: the context window. How many examples you could give the model was however many you could fit in the prompt, and these windows used to be narrow. You couldn't put more than a handful of examples. With the arrival of long context windows, this limit lifted. We can now work with windows of hundreds of thousands, even millions of tokens — and this means being able to put dozens, hundreds, thousands of examples in the prompt. This is exactly what many-shot in-context learning is: few-shot with the number of examples radically increased.

This quantitative change leads to a qualitative leap. With a few examples the model grasps the general idea of the task, while with many examples it also learns the task's subtleties, exceptions, edge cases. From what I've seen in the field, when set up correctly, many-shot can give results of a quality that rivals fine-tuning on some tasks — and it does this without touching the model weights at all, just with the prompt. This is tremendous flexibility for organizations.

Understanding why many-shot is so effective is the key to using it correctly. A few mechanisms work together.

First, pattern-learning depth. With three examples the model guesses "I think it wants something like this." With three hundred examples, it sees the true distribution of the task: which cases are frequent, which rare, how exceptions are handled. This is a shift from guessing to knowledge. The model no longer "senses" the task, it "learns" from examples.

Second, format and tone consistency. Many examples show the model exactly how the output you want should look. Format, length, tone, terminology — all of these are grasped far more consistently with many examples. With few examples the model sometimes misses the format; with many it almost never does.

Third, covering edge cases. The difficulty of real tasks usually lies in rare, odd cases. With few examples you can't show these edge cases to the model; with many you can. The model sees the answer to "what to do in this odd case" in the examples. This is critical for production quality, because systems usually break precisely on these edge cases.

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Field observation: The best thing about many-shot is that it's teachable. When a task is hard to describe in words — the "I can't describe the tone I want but you'll get it when you see an example" situations — examples are far more powerful than language. A good example set communicates far more clearly than a long list of instructions.

Here's the most practical question. I have labeled examples; should I put them in the prompt (many-shot) or train the model (fine-tuning)? Both are "learning from examples" but come with very different trade-offs.

Many-shot's advantages: it works immediately (no training process), it's flexible (you can change examples instantly), and it's model-agnostic (you can try the same example set on different models). Its disadvantage: because the examples enter the prompt on every request, token cost and latency rise; and with very large example sets you can hit the context window limit.

Fine-tuning's advantages: because examples are "permanently" baked into the model, you don't have to send them again on every request — this is cheaper and faster at inference; and it can process very large datasets (thousands, tens of thousands of examples). Its disadvantage: it requires a training process (time, expertise, cost), isn't flexible (retraining needed when something changes), and is tied to a specific model.

The decision rule I apply in the field: try many-shot first. Fast, flexible, and usually sufficient. If you reach acceptable quality with many-shot, you don't need to enter the complexity of fine-tuning at all. Move to fine-tuning only in these cases: if many-shot doesn't give sufficient quality, if your example count is too large to fit in the context window, or if inference cost/latency is critical and you don't want to send examples every time. So fine-tuning isn't a starting point but a next step resorted to where many-shot doesn't suffice.

Dimension	Many-shot	Fine-tuning
Startup speed	Instant	Training process needed
Flexibility	Change examples instantly	Change = retraining
Inference cost	High (examples every request)	Low (examples baked in)
Example capacity	Limited by context window	Very large data processable
Model dependence	Model-agnostic	Tied to a specific model

Many-shot's biggest practical obstacle is cost. If you put hundreds of examples in the prompt on every request, you pay for those examples' tokens over and over for each request. In a high-volume system this quickly inflates the bill. But good news: this problem has an elegant solution — prompt caching.

The idea: in your many-shot prompt the example set is usually fixed; only the actual question changes. So in the structure "here are 300 examples + [the user's question]," the first 300 examples are the same on every request. Prompt caching processes this fixed prefix once and caches it; on subsequent requests, instead of reprocessing those examples, it continues from where it left off in the cache. This dramatically lowers both cost and latency.

It's no exaggeration to say prompt caching makes many-shot economical. Without caching, using a prompt with hundreds of examples at high volume is often not economical. With caching, the fixed example set's cost drops to nearly one-time and the marginal request cost falls to just the variable part (the actual question). So if you'll use many-shot, embed prompt caching into the architecture from the start; these two work together and shouldn't be thought of separately.

In many-shot, is "more examples always better"? No, it's not that simple. The quality and diversity of examples matter as much as their number, sometimes more.

There are a few principles in example selection. First, representativeness: your examples must reflect the distribution of the real task. If you only put easy cases, the model struggles on hard ones. The example set must include both frequent cases and rare edge cases. Second, diversity: a hundred examples very similar to each other teach less than twenty examples different from each other. Diversity shows the model the breadth of the task. Third, quality: every example must be correct. A wrong or inconsistent example misdirects the model — indeed a few bad examples can spoil the effect of many good ones.

An advanced technique is dynamic example selection: instead of a fixed example set, selecting the examples most similar to each incoming question with a search mechanism and placing them in the prompt. This is applying RAG logic to many-shot. So each question is supported with the examples most relevant to it. This is a more complex architecture but can make a clear difference on hard tasks. On simple tasks a fixed good set suffices; on complex and varied tasks dynamic selection is valuable.

A little-known but important topic in many-shot is the order of examples. Models don't use information equally well in long context; they usually remember information at the beginning and end better than in the middle. This is called the "lost in the middle" problem and directly affects many-shot.

The practical consequence: put the most important, most representative examples at the beginning and end of the prompt; place less critical ones in the middle. Also, placing the actual question right after the examples, at the end of the prompt, usually gives the best result because the model pays more attention to what it saw last. These fine tunings can make a difference even with the same example set.

Also remember that the return on increasing the number of examples indefinitely diminishes. Beyond a certain point, adding more examples doesn't improve quality but increases cost and the "lost in the middle" risk. The optimal number of examples differs for each task and you must find it by measuring. Not "more is always better"; "enough, correctly selected, and well ordered" is best.

For concreteness, let me walk through a typical many-shot setup step by step. Say you're building a system for a customer-service team that sorts incoming complaints into certain categories and produces a draft reply to each in the brand's voice. This is the kind of task few-shot struggles with but many-shot shines at: it requires both classification and generation, and a very specific tone.

First, you gather a representative example set from past complaint-reply pairs — real examples from different categories, different difficulty levels, well reflecting the brand's voice. Say 150 examples. You put these in the prompt, but not randomly: the most representative and hardest examples at the beginning and end, the middle ones in between. You cache the example set with prompt caching so it isn't reprocessed on every request. Each new incoming complaint is appended after the examples, at the end of the prompt.

Then you measure the system with a golden set (say 40 real complaints and their ideal replies). Is the classification correct? Is the reply's tone on-brand? Is the format consistent? You find where it's weak, add new examples representing those cases, and measure again. In a few iterations, without fine-tuning, just with examples, you reach production quality. And the best part: when the brand voice changes or a new complaint type emerges, instead of retraining the model you just update the example set. This flexibility is the essence of many-shot's enterprise appeal.

Many-shot uses the context window intensively, and this requires care. While filling the window with examples, you must also leave room for the actual task, relevant information, and the model's "thinking" space. If you clog the window entirely with examples, the model may not find space to focus on the actual question, or quality may drop in long context.

So think with a "budget" mindset: your context window is a limited resource and you're sharing it among examples, instructions, relevant information, and output. Each example takes space in the window and has a cost. The goal is to teach the most with the fewest examples — that is, ensuring each example truly contributes. Examples that don't contribute, that repeat, only consume the window and the budget.

Another reality of long context: as the window grows, the model's ability to use all of that window equally well doesn't always rise proportionally. Having a million-token window doesn't mean the model will use that million tokens perfectly. So the right approach isn't "fill the window as much as I can" but "put as many examples as this task really needs." Window capacity is a ceiling, not a target.

A final strategic observation. Many-shot's rise doesn't make fine-tuning entirely needless; but it clearly narrows its territory. In many scenarios where we once said "fine-tuning is essential to do this task well," many-shot is now sufficient. This is a big convenience for organizations, because fine-tuning is a serious investment: data preparation, training infrastructure, expertise, and retraining with every change.

Fine-tuning is still indispensable in certain places: if your example count is too large to fit the context window, if inference cost/latency is critical and you don't want to send examples every time, or if a very deep change in the model's behavior is needed. But these scenarios are rarer than before. For most enterprise tasks, a well-built many-shot gives sufficient quality without entering fine-tuning's complexity.

My recommendation is to think of this as a "which first" order: zero-shot → a few examples → many-shot → (if needed) fine-tuning. Stay as low as possible on this ladder; move up only when a lower rung doesn't suffice. This discipline keeps both cost and complexity at a minimum and gives you a clear quality measurement at each level. Starting with the most expensive and most complex solution is usually needless.

Working with Turkish content, many-shot is especially valuable, but there are a few subtleties. First, use Turkish examples. Giving the model English examples and expecting Turkish output leads to inconsistency. If the task is Turkish, the examples must be Turkish too so the model grasps the Turkish tone, terminology, and flow you want.

Second, Turkish's nuances — the formal/informal tone distinction, idioms, cultural context — are hard to describe in words but communicated very clearly with examples. This is where many-shot is especially strong in Turkish: the best way to say "this is how our brand's voice speaks" is to show many examples written in that voice. A list of instructions can say "be warm and friendly," but ten examples actually teach it.

Third, Turkish can be more "expensive" than English in tokens; the same content sometimes takes more tokens in Turkish. This slightly increases many-shot cost and makes prompt caching even more important. If you use Turkish many-shot, be sure to enable caching so the cost stays sustainable.

One of 2026's most interesting observations is how many-shot interacts with reasoning models. Reasoning models "think" step by step before answering. When using many-shot with these models, having your examples in the form "input-thinking steps-output" rather than just "input-output" usually gives a stronger result. That is, you show the model not just the correct answer but how it was reached.

This is the combination of chain-of-thought with many-shot. When you explicitly show reasoning in your examples, the model learns not just "what" but "how" to think. On tasks like complex analysis, multi-step problem solving, legal or financial reasoning, this difference can be large. A few examples with reasoning fully shown are sometimes more effective than many examples containing only answers.

But there's a balance: because reasoning models already "think" on their own, on some simple tasks too many examples can be needless or even harmful — the model can over-attach to a specific thinking pattern in the examples and constrain its own reasoning. So when working with reasoning models, carefully tune the number and type of examples to task complexity. Sometimes few but quality examples beat many but constraining ones.

Many-shot is powerful but not every task requires it. For some tasks a few examples — even zero examples (zero-shot) — are more than enough. Overdoing it and putting hundreds of examples on every task means needless cost and complexity. The right question: does this task really require many examples?

For simple, well-defined tasks the model already knows well, few examples suffice. For example, modern models already do tasks like summarizing a text or classifying a sentiment well with zero or a few examples. Investing in many-shot here is waste. Many-shot's real value emerges when the task is novel, niche, or requires a very specific format/tone — that is, tasks specific to your work that the model didn't see much in training.

Practical approach: always start with the least. First try zero-shot. If it's not enough, add a few examples. If still not enough, go up to many-shot. This gradual approach protects you from loading excessive examples onto every task and gives you the chance to see the cost-quality balance at each level. Starting with the most expensive solution is usually needless engineering.

Combining two powerful techniques is often better than either alone. Thinking of many-shot and RAG together is very valuable, especially on complex enterprise tasks. The idea: dynamic example selection is actually a RAG problem — retrieving the examples most relevant to the incoming question from an example pool and placing them in the prompt.

In this combined approach, on one side runs information retrieval (classic RAG: retrieve documents related to the question), on the other example retrieval (retrieve solved examples similar to the question). The model accesses both relevant information and similar examples at once. This gives a strong result especially on tasks requiring "both information and format" — for example, answering a customer question based on both the relevant policy document and similar past answers.

When building this architecture, keep your example pool as a separate index and retrieve the most relevant examples for each question. So your prompt always fills with the examples most suited to that question, not bloated with needless examples. This both increases quality and keeps token cost under control — because instead of a fixed hundreds of examples, you use a small number of highly-relevant examples specifically selected for that question. A clever combination covers the weakness of both techniques.

Let me gather the mistakes I see frequently in this area. First, using low-quality examples: inconsistent, wrong, or careless examples misdirect the model even when numerous. A few bad examples can spoil the whole flock. Second, skipping prompt caching: using many-shot without caching at high volume needlessly inflates the bill; the two should be thought of together. Third, blindly piling on examples: putting hundreds of examples needlessly on the assumption "more is always better" increases cost and "lost in the middle" risk but not quality.

Fourth, ignoring ordering: the order of examples affects quality; not putting the most important ones at the beginning and end means worse results with the same examples. Fifth, using English examples in a Turkish task: language inconsistency spoils output quality. Sixth, deciding without measuring: iterating by the feeling "I think it got better" is usually misleading; without a golden set you can't see real progress. The common lesson of these mistakes is that many-shot is not "magic" to apply thoughtlessly but a technique requiring discipline.

An under-discussed dimension of many-shot is example security. The examples you put in the prompt usually come from real data — real customer questions, real documents, real answers. This creates two risks and both must be managed from the start.

First, KVKK. If your examples contain personal data (customer name, contact info, sensitive detail), sending them to a model is a data-processing activity. If you use a foreign API, this is a cross-border transfer. So anonymize your examples or clean sensitive information before putting them in the prompt. Embedding a real customer example as-is in the prompt can unknowingly disclose personal data. Using anonymized but still instructive examples protects both KVKK and quality.

Second, the example-poisoning risk. If your example set is fed automatically from unaudited sources, malicious or faulty examples can slip into the set and distort the model's behavior. Especially if you use dynamic example selection (retrieving examples from a pool), that pool's security is critical. Feed your example pool from audited, trusted sources and review it regularly. A few poisoned examples can spoil the quality and security of the whole system.

Managing these two risks isn't hard but mustn't be neglected. Anonymize examples, keep the example pool secure, and audit it regularly. Many-shot is a powerful technique; but its power is only as good as the quality and security of the examples it uses. Garbage examples, garbage output; insecure examples, insecure system.

Don't build many-shot once and forget it; measure and improve it. Create a golden set (test tasks and their correct answers) and measure your many-shot prompt against this set. How does quality change when you change the number, selection, and ordering of examples? Without this measurement, you decide by the feeling "I think it got better," which is usually misleading.

The iteration process works like this: start with a base example set, measure on the golden set, find the task types the model fails on, add examples representing those types, measure again. This loop sharpens your example set over time. Usually, adding a few targeted good examples is far more effective than adding a hundred random ones. Not quantity, but the right quantity matters.

One final reminder: don't see many-shot as a "cure-all." It's a powerful but single tool in your prompt-engineering toolkit. On some tasks zero-shot suffices, on some many-shot shines, on some fine-tuning is needed. Mastery lies in matching the right tool to each task. Just as a carpenter doesn't hit everything with a hammer, don't pile hundreds of examples onto every task; measure what the task really requires and decide accordingly. This awareness leads you to both more economical and higher-quality results.

To sum up: many-shot in-context learning is a powerful tool the long-context era gave us. Getting quality close to fine-tuning just with examples, without entering fine-tuning's complexity, is tremendous flexibility for organizations. The way to use it correctly: try many-shot first, manage cost with prompt caching, select examples representative and diverse, put the most important ones at the beginning and end, use Turkish examples in Turkish tasks, and measure and improve everything with a golden set. With this discipline, you combine few-shot's simple power with long context's scale and extract far more from the model you have.