Location>code7788 >text

Practical practice of learning with few samples: Few-Shot Prompt design

Popularity：401 ℃/2025-03-07 12:06:41

Let AI learn to learn to learn from one example by one

Imagine you want to teach an alien to know animals on Earth. If you only show it three photos of the Husky, it might think that all four-legged animals are called "Husky". This is the real situation of AI models in learning with few samples - they are like aliens who have just arrived on Earth, and you need to guide your cognition through carefully designed "prompt words". Today we will talk about how to use Few-Shot Prompt design to enable AI to achieve the most accurate understanding with the smallest learning samples.

Example choice: AI also requires "nutritional balance"

Case 1: Suppose we want AI to learn to judge the emotional tendency of restaurant reviews

Error demonstration：

Review: The steak at this restaurant is tender and juicy
 Emotion: Positive

 Comment: The waiter is very friendly
 Emotion: Positive

 Comment: The decoration style is very stylish
 Emotion: Positive

Correct demonstration：

Comment: Steak is like a leather sole, but dessert is a work of art → Emotion: Neutral
 Comment: The dishes are average but the price is comparable to Michelin → Emotion: Negative
 Comment: Although waiting for 1 hour, every dish is worth waiting → Emotion: Positive

Good examples should be like palettes：There are straightforward praises，There are also tactful complaints；It must contain clear emotional words（"Can be regarded as"、"Comparable"），There must also be expressions that require reasoning（"Waiting1Hour but worth it"）。 Just like preparing lunch boxes for your child, you cannot be full of meat or vegetables, you should pay attention to nutritional matching.

Diversity formula list：

Dimension Example description Case
Complexity of sentence structure Simple sentences/compound sentences/inverted sentences "Excuse me!" vs "Although...but..."
Emotional intensity Strong/neutral/implicit "Difficult to swallow" vs "not tolerate your appetite"
Expression method Direct description/metaphor/exaggeration "Service snail speed" vs "wait for a long time"

Comparative training method of positive and negative samples: AI's "fault-finding game"

Imagine you are teaching your child to distinguish between cats and dogs. If you only show the correct samples, it is like only showing "Cats and Dogs". But if the case of mistakenly thinking of a fox as a cat is shown at the same time, the child's discernment ability will improve by leaps and bounds. The same is true for AI, which requires comparative stimulation of positive and negative cases.

Code generation case：

#Positive example: Canonical Python functions
 def calculate_average(numbers):
     total = sum(numbers)
     return total / len(numbers)

 # Counterexample: No processing of zero-deletion error
 def buggy_average(nums):
     return sum(nums)/len(nums) # Crash when nums is empty

Customer service reply comparison：

User: Order number 1234 has not been received yet
 Demonstration: Recorded → (correct but indifferent)
 Good demonstration: 1234 logistics orders have been queried and are expected to be delivered tomorrow. The latest logistics screenshots are attached (providing specific information)

This kind of contrast training is like installing a "unsense-proof mechanism" for AI: when it wants to write sum(nums)/len(nums), it will suddenly remember the counterexample that causes the program to crash, and automatically add the judgment of if len(nums)>0.

Data pollution prevention and control: Create a "sterile operating table" for AI

In the medical field, a contaminated petri dish could ruin the entire experiment. In AI training, a wrong sample is like a dripping in clean water, which will produce exponential diffusion.

Lessons of blood and tears：
When a medical AI analyzed CT films, the pictures of "malignant tumors" in the training samples were all marked with red boxes. As a result, what I learned was not to identify the tumor, but to detect the red boxes - it can be called "buy a box and return a bead" in the AI ​​industry.

Three-piece protective set：

  1. Sample disinfection: Check each example like picking fruit in the supermarket

    • Exclude specific logos (watermarks, logos)
    • Verify the accuracy of facts (especially numbers, proper nouns)

  2. Source Quarantine: Establish a "food traceability system"

    [Sample source trustworthiness rating table]
 ✔️ Authoritative paper data → ★★★★★★
 ✔️ Verified user feedback → ★★★★☆
 ❌ Anonymous forum content → ★★☆☆☆

  3. Freshness detection: Apply "Shelf life label" to the sample

    [Technology Sample] Validity period: 6 months (Moore's Law Period)
 [Laws and Regulations] Validity period: Dynamic adjustment according to the revision time
 [Cultural Memories] Validity period: It may be out of date at any time (refer to the popularity cycle of "yyds")

Practical drill: Hand-in-hand design prompt words

Scene: Design intelligent customer service speech for cross-border e-commerce

Step1: Diversified Example Collection

User: The package is signed for but not received
 Excellent reply: I have contacted the local logistics company and the delivery staff said it was placed in the property front desk (providing specific solutions)

 User: Will this sweater pill?
 Excellent reply: Anti-pilling process is adopted, but severe friction may still cause slight pilling (objective instructions + prevention suggestions)

Step2: Positive and negative samples PK

Bad reply: Please wait patiently (empty)
 Good reply: The customs clearance documents of your package have been completed, and it is expected to be delayed by 3 days (specific information + solution)

Step 3: Pollution prevention and control
Delete cases containing specific repository numbers and replace timeliness information:

Original sample: expected to be delivered 7 days (using DHL)
 After update: the current logistics time is 5-12 working days (no carrier specified)

Common Traps and Cracking Tips

Trap 1: Sample "inbreeding"
Error: All translation examples are "Chinese → English"
Cracked: Add cross-lingual samples such as "English → Spanish", "Japanese → Chinese" and other

Trap 2: Greenhouse Flower Syndrome
Error: All customer service conversations are in standard Mandarin
Cracked: Add "Emergency! The package is spicy!" (online term), "Why hasn't my dialect yet" (dialect)

Trap 3: Boat carving and sword training
Error: Use 2020 epidemic policy to answer current questions
Cracking: Create a sample "fresh-keeping calendar" and automatically detect expired information every month

Effectiveness Test: Your AI Graduation Exam

Design a "stress test package" to test AI like a game boss level:

Level 1: Semantic Maze
"You said you will reach the next three days, and now it's 23:59 on the third day!" (Test your time understanding ability)

Level 2: Speak up
"Thank you for allowing me to experience the fun of waiting for express delivery for half a month" (test irony recognition)

Level 3: Cross-border Lenovo
"My package may have traveled to Antarctica with the penguin" (testing metaphorical processing)

AI that has passed these tests is like a student who has passed the final exam and can take up the job with confidence.

Learning with few samples is not about making AI lazy, but about teaching it to "get a big deal". A good prompt word design is like giving AI a pair of smart glasses: it will not crush it with massive data, but can also help it accurately focus on key features. Remember, AI is like a particularly picky child - it doesn’t matter how much you feed, but how well you feed. When you master these design methods, you can use the most economical samples to cultivate the "smart" AI assistant.

<Previous Next>