This Prompting Trick Makes AI Respond Faster with 96% Accuracy

Unlocking the real-world power of Few-Shot prompting in multimodal AI

The Hidden Power of Better Instructions

What if there was a simple trick that could help AI answer faster—and get the answer right nearly every time?

No retraining. No fine-tuning. Just a smarter way to ask.

A recent study from CeADAR, Ireland’s Centre for AI found that Few-Shot prompting, a deceptively simple strategy, dramatically boosts performance in Multimodal Large Language Models (MLLMs)—delivering up to 96.88% accuracy in code generation, while reducing hallucinations and slashing response times.

“The key isn’t in making the model smarter—it’s in making the instructions clearer.”

Let’s break down how this works, why it matters, and how you can use it today.

What Are MLLMs—and Why They Matter

Multimodal Large Language Models are built to do more than read—they see, reason, and respond to diverse data types like text, images, and code.

They’re made up of:

• A vision encoder (e.g., ViT, CLIP, SigLIP)
• A language model backbone (like LLaMA, Gemma, or Qwen)
• A transformation layer that fuses those inputs

These models are used in real-world tools that:

• Read and explain charts or maps
• Generate code from diagrams
• Summarize images and documents
• Assist with medical, scientific, and financial analysis

But their magic doesn’t just come from architecture—it comes from how you talk to them.

“With the right prompt, the same model can go from clumsy assistant to expert collaborator.”

The Prompting Strategies Explored

The study compared seven prompting techniques across 13 open-source MLLMs and 24 tasks:

1. Zero-Shot – Just instructions, no examples
2. One-Shot – One example included
3. Few-Shot – A few examples shown before the question
4. Chain-of-Thought (CoT) – The model is asked to explain its reasoning step-by-step
5. Analogical – Uses analogy-based reasoning
6. Generated Knowledge – Adds helpful background facts
7. Tree-of-Thought (ToT) – The model explores multiple reasoning paths before deciding

These were tested on tasks spanning:

• Logical Reasoning
• Multimodal Interpretation
• Code Generation
• Factual Knowledge Integration

The Breakthrough: Few-Shot is Surprisingly Superior

While many assume complex reasoning methods like CoT or ToT are the gold standard, this study showed that Few-Shot prompting quietly outperforms them across multiple domains.

Especially in Code Tasks

• 96.88% accuracy achieved by Few-Shot prompting on code generation
• Near-zero hallucinations in outputs
• Significantly faster than Tree-of-Thought or Generated Knowledge methods

“Few-Shot prompting consistently delivered clean, concise, and correct outputs—without the extra baggage.”

Where complex prompts bogged down smaller models or increased error rates, Few-Shot found the sweet spot between guidance and efficiency.

How to Use Few-Shot Prompting Like a Pro

So, Few-Shot prompting is faster, more accurate, and less prone to hallucinations. But how do you actually do it right?

Here’s how to turn this technique into a reliable tool in your AI toolkit—complete with simple templates and real examples.

🧠 What Makes a Good Few-Shot Prompt?

Few-Shot works by showing the model 2–5 well-structured examples before presenting the actual task.

Think of it like this:

“You’re not telling the AI what to do—you’re showing it how it’s been done before.”

Each example should mirror the structure and tone of the desired output. You’re setting a rhythm the model can follow.

💡 Few-Shot Prompt Template

Here’s a basic template you can customize:

Example 1:  
Input: [image or text description]  
Task: [what is the goal?]  
Output: [expected result]  

Example 2:  
Input: [image or text description]  
Task: [goal]  
Output: [expected result]  

Now you try:  
Input: [new image or scenario]  
Task: [same goal]  
Output:

🧪 Real Example – Code Generation from an Image

Use Case: Converting a visual table into a Python dictionary

Prompt:

Example 1:  
Input: An image showing a table of fruit and prices  
Task: Extract the data into a Python dictionary  
Output:  
{"Apple": 1.2, "Banana": 0.5, "Orange": 0.8}

Example 2:  
Input: An image showing a chart with product names and stock counts  
Task: Convert it into a Python dictionary  
Output:  
{"Laptop": 15, "Mouse": 40, "Monitor": 10}

Now you try:  
Input: [new image of a shopping list]  
Task: Convert it into a Python dictionary  
Output:

✅ This kind of pattern helps the model stay consistent and reduces the chances of outputting malformed or hallucinated code.

🎨 Real Example – Visual Storytelling

Use Case: Merging text and an image into a short narrative

Prompt:

Example 1:  
Input:  
Text: "A boy holding a kite."  
Image: A windy day with trees bending.  
Task: Write a short story combining both.  
Output:  
"As the wind picked up, Sam gripped his kite tighter. The sky danced with color as he ran across the field."

Example 2:  
Input:  
Text: "A cat looking at a goldfish bowl."  
Image: A curious cat on a windowsill.  
Task: Write a short story combining both.  
Output:  
"Luna stared through the glass, mesmerized. The goldfish darted like flickers of sunlight, just out of reach."

Now you try:  
Input:  
Text: "A girl finding a map under the floorboards."  
Image: A dusty attic with scattered boxes.  
Task: Write a short story combining both.  
Output:

📌 This setup lets the model “see the pattern” and replicate a coherent, creative output.

⚙️ Tips for Better Results with Few-Shot

• Keep your examples diverse but consistent – Mix content, not format.
• Don’t overload – 2–4 examples are usually enough.
• Mirror your task exactly – If your input includes an image and text, show both in examples.
• Use clear, concise outputs – Fluffy answers lead to fluffy results.

“Think of Few-Shot prompts like flashcards—each one helps the model remember the right way to respond.”

Few-Shot prompting isn’t magic—but when crafted carefully, it often feels like it.

A Closer Look: Performance by Task Type

🧠 Reasoning & Compositionality

• Large models thrived here—Few-Shot prompting gave them an edge
• Small models struggled with complex prompts like ToT, showing hallucination rates of 75%+
• Simpler prompts reduced noise and boosted clarity

🖼️ Multimodal Understanding

• Zero-Shot and One-Shot achieved 100% relevance on some models
• Adding complexity often reduced performance
• Few-Shot added minimal gain here, proving less is sometimes more

“Not every task needs a roadmap—sometimes all it takes is a clear, single instruction.”

💻 Code Generation & Execution

• This was Few-Shot’s home turf
• Large MLLMs generated correct, executable code with near-perfect accuracy
• Tree-of-Thought and Generated Knowledge introduced latency and redundancy

📚 Knowledge Retrieval

• Large models using Zero-Shot prompts reached up to 87.5% accuracy
• Smaller models hallucinated up to 40% under heavy, structured prompts
• Simpler strategies consistently outperformed their more elaborate cousins

Why Does Few-Shot Work So Well?

Few-Shot prompting shines because it shows, not tells.

By giving the model a few clear examples, you anchor its thinking. It doesn’t have to guess what format you want, or what logic applies—it just mimics the pattern.

“Like showing someone three winning lottery tickets and asking them to pick a fourth—context becomes clarity.”

Meanwhile, prompts that ask models to simulate reasoning (CoT, ToT) often lead to:

• Longer processing times
• Higher error rates (especially in small/medium models)
• Outputs that sound smart but lack substance

Few-Shot avoids that by doing something surprisingly human: it teaches by example.

Applying the Trick: When (and How) to Use Few-Shot Prompting in Real Projects

You know Few-Shot prompting works—but how do you decide when to use it? And when to avoid it?

Let’s break it down clearly so you can apply it with confidence.

✅ When Few-Shot Prompting Works Best

🧠 Use it for structured or logic-heavy tasks.
If you’re generating code, interpreting charts, or solving visual problems—Few-Shot gives the model just enough direction to nail it.

🚀 It’s ideal for mid-to-large models (4B+ parameters).
Larger models can learn from examples and apply them smoothly, without getting overwhelmed.

🛡️ You want low hallucination and consistent formatting.
Few-Shot excels when accuracy matters—like medical summaries, financial data, or step-by-step reasoning.

“Few-Shot helps models think clearly, not creatively. It’s about precision over poetry.”

🛑 When to Keep Prompts Simple

💡 If the task is obvious or perception-based, like captioning an image or matching a sentence to a visual, Zero- or One-Shot prompting usually works better.

📉 Smaller models (under 4B) tend to get confused by structured prompts. They respond better to short, direct instructions.

🎨 Creative tasks don’t benefit as much.
When you want open-ended storytelling, emotional tone, or unexpected output, over-structuring can stifle creativity.

“Not every prompt needs scaffolding. Sometimes, all it takes is a well-phrased question.”

🔄 Adaptive Prompting: The Smarter Strategy

Instead of sticking to one prompt style, start asking:
What’s the task? Who’s the model? What do I need out of it?

This mindset is what makes prompt engineering truly effective.

Here’s a quick cheat sheet to guide you:

🧪 Prompting Decision Matrix

In short:

• Use Few-Shot when structure, logic, or formatting matters.
• Use simpler prompts when tasks are intuitive or visual.
• Match the prompt to the model’s capacity and the task’s complexity.

“Prompting isn’t about using the fanciest technique—it’s about choosing the right one for the job.”

The Bigger Picture: Prompting as UX for AI

This study flips the narrative: it’s not always about the best model, but the best way to use it.

Prompt engineering is becoming the UX layer for AI. It controls:

• The clarity of outputs
• The speed of interaction
• The reliability of results

As models become general-purpose, prompting becomes your tool to make them feel specific, helpful, and human.

“The prompt isn’t just input—it’s the interface.”

Final Takeaway: Use the Trick, Unlock the Performance

You don’t need more data. You don’t need more compute.

You need better prompts.

This study proves that Few-Shot prompting:

• Boosts accuracy
• Reduces hallucination
• Speeds up AI responses
• Keeps outputs concise and usable

So if you’re building apps, assistants, or multimodal AI tools—don’t ignore the prompt. Refine it, test it, and let it do the heavy lifting.

“Smarter AI starts with smarter prompting.”

Source Credit

Study by Anwesha Mohanty, Venkatesh B. Parthasarathy, and Arsalan Shahid of CeADAR, University College Dublin. Read the full paper on arXiv.