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Day 7 · ~12 min●

Yesterday: "think step by step". Today: also show the model what step-by-step looks like for your task. Two solved examples teach the format better than any instruction.

python

prompt = '''Classify each statement as fact or opinion. Show your reasoning briefly, then give the label.

Statement: "The Pacific Ocean is the largest ocean."
Reasoning: This is a verifiable geographic measurement.
Label: fact

Statement: "Chocolate is the best ice cream flavour."
Reasoning: This is a personal preference, not measurable.
Label: opinion

Statement: "Water boils at 100 degrees Celsius at sea level."
Reasoning:'''

result = Agent(model).run_sync(prompt)
print(result.output)

The two examples teach the format — Reasoning then Label — and the kind of reasoning. The model continues the pattern.

Right. Few-shot prompts give the model concrete templates. With CoT alone, the model picks its own format for the reasoning trace; with few-shot CoT, you pick the format and the model copies it. Easier to parse afterwards.

How many examples?

2-3 is the sweet spot. One example often isn't enough to disambiguate the pattern. Five+ starts hurting (longer prompts, marginal returns). The classic paper uses 8 examples; for production tasks 2-3 is what most teams ship with.

Few-shot chain-of-thought

Combine two patterns:

Few-shot prompting (Foundations day 11) — examples in the prompt teach format
Chain-of-thought (yesterday) — model reasons before answering

Anatomy of a few-shot CoT prompt

[task description]

[example 1 input]
Reasoning: [example 1 reasoning]
Label: [example 1 answer]

[example 2 input]
Reasoning: [example 2 reasoning]
Label: [example 2 answer]

[real input]
Reasoning:

Note how the prompt ends mid-pattern — "Reasoning:" with nothing after it. The model is forced to continue the structure, producing reasoning then label.

What examples teach

Element	What the model learns
The example input format	What kind of input to expect
The reasoning style	How long, how structured, what to focus on
The label format	One word? A phrase? A choice from a closed set?
The full sequence	Reasoning before label, not after

If your examples have one-sentence reasoning, the model produces one-sentence reasoning. If your labels are lowercase, the model produces lowercase. The discipline of the examples becomes the discipline of the output.

Picking examples

Diverse — examples should span the range of inputs you expect (different categories, different lengths)
Edge cases — include one tricky case so the model knows what to do with ambiguity
Same domain — if the task is about scientific facts, the examples should be too

Parsing the output

After Reasoning: ... Label: fact comes back, extract the label:

python

import re
match = re.search(r"Label:\s*(\w+)", result.output, re.IGNORECASE)
label = match.group(1).lower() if match else None

The few-shot format makes this regex parse possible — the model committed to your format because the examples did.

Sign up to practice

Create a free account to get started. Paid plans unlock all tracks.

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Day 7 · ~12 min●

Yesterday: "think step by step". Today: also show the model what step-by-step looks like for your task. Two solved examples teach the format better than any instruction.

python

prompt = '''Classify each statement as fact or opinion. Show your reasoning briefly, then give the label.

Statement: "The Pacific Ocean is the largest ocean."
Reasoning: This is a verifiable geographic measurement.
Label: fact

Statement: "Chocolate is the best ice cream flavour."
Reasoning: This is a personal preference, not measurable.
Label: opinion

Statement: "Water boils at 100 degrees Celsius at sea level."
Reasoning:'''

result = Agent(model).run_sync(prompt)
print(result.output)

The two examples teach the format — Reasoning then Label — and the kind of reasoning. The model continues the pattern.

Right. Few-shot prompts give the model concrete templates. With CoT alone, the model picks its own format for the reasoning trace; with few-shot CoT, you pick the format and the model copies it. Easier to parse afterwards.

How many examples?

2-3 is the sweet spot. One example often isn't enough to disambiguate the pattern. Five+ starts hurting (longer prompts, marginal returns). The classic paper uses 8 examples; for production tasks 2-3 is what most teams ship with.

Few-shot chain-of-thought

Combine two patterns:

Few-shot prompting (Foundations day 11) — examples in the prompt teach format
Chain-of-thought (yesterday) — model reasons before answering

Anatomy of a few-shot CoT prompt

[task description]

[example 1 input]
Reasoning: [example 1 reasoning]
Label: [example 1 answer]

[example 2 input]
Reasoning: [example 2 reasoning]
Label: [example 2 answer]

[real input]
Reasoning:

Note how the prompt ends mid-pattern — "Reasoning:" with nothing after it. The model is forced to continue the structure, producing reasoning then label.

What examples teach

Element	What the model learns
The example input format	What kind of input to expect
The reasoning style	How long, how structured, what to focus on
The label format	One word? A phrase? A choice from a closed set?
The full sequence	Reasoning before label, not after

If your examples have one-sentence reasoning, the model produces one-sentence reasoning. If your labels are lowercase, the model produces lowercase. The discipline of the examples becomes the discipline of the output.

Picking examples

Diverse — examples should span the range of inputs you expect (different categories, different lengths)
Edge cases — include one tricky case so the model knows what to do with ambiguity
Same domain — if the task is about scientific facts, the examples should be too

Parsing the output

After Reasoning: ... Label: fact comes back, extract the label:

python

import re
match = re.search(r"Label:\s*(\w+)", result.output, re.IGNORECASE)
label = match.group(1).lower() if match else None

The few-shot format makes this regex parse possible — the model committed to your format because the examples did.

Sign up to practice

Create a free account to get started. Paid plans unlock all tracks.

or