🧠 The Secret Sauce for Cooking Up Killer GPTs

🛠️ 10 Hacks to Build Insanely Good Custom GPTs

1. Use Trigger/Instruction Pairs with Delimiters

What it is: Break down each multi-step task into pairs of “Trigger:” and “Instruction:” lines, separated by a clear delimiter (like ---).
Why it matters: This prevents the model from merging or skipping steps, making the execution rock-solid and predictable.

2. Persona Priming & Tone Locking

What it is: Start your instructions with a clear persona line that sets the voice, expertise, and tone.
Why it matters: This keeps the GPT’s responses consistent in style and vocabulary throughout the session.

This ensures the GPT answers with the right level of detail and clarity.

3. Use Structured Markdown Formatting

What it is: Format your instructions using Markdown elements like headings (#), subheadings (##), bullet points, bold, and italics.
Why it matters: The model recognizes these visual cues and adheres to your layout, enhancing readability and fidelity.

4. Explicitly Define Capabilities & Limitations

What it is: In your configuration, clearly list what your GPT can and cannot do.
Why it matters: This keeps outputs safe, on-brand, and prevents the model from venturing into unwanted areas.

5. Targeted Knowledge Chunking

What it is: Instead of uploading huge manuals, split relevant content into smaller, named files and reference them specifically.
Why it matters: This avoids context overload and speeds up retrieval, making answers more accurate.

6. Provide Few-Shot “Good vs Bad” Examples

What it is: Show the GPT one example of a bad response and one of a good response for the same prompt, clearly labeled.
Why it matters: This teaches the model what to avoid and what to emulate, improving output quality.

7. Meta-Prompt for Builder Feedback

What it is: Ask the GPT itself to review and improve your instructions before finalizing them.
Why it matters: This surfaces blind spots and clarifies instructions you might have missed.

8. Embed a Self-Critique Prompt

What it is: End your instruction block with a reminder for the GPT to check its answer for completeness and accuracy before responding.
Why it matters: This triggers an internal review, reducing hallucinations and omissions.

9. Pre-Chunk & Name Your Knowledge Files

What it is: Organize your reference documents into 3–5 smaller files with clear names, and instruct the GPT to refer to them by name.
Why it matters: This prevents the model from being overwhelmed by too much data and improves focus.

10. A/B Test by Cloning with Micro-Tweaks

What it is: Clone your GPT, change one line or instruction slightly, then test both versions side-by-side with the same prompts.
Why it matters: This helps identify which tweaks improve performance before finalizing your GPT.

Putting It to Use: Building a Recipe Assistant GPT

Let’s walk through creating a Recipe Assistant GPT using these tips. This GPT will help users plan meals based on the ingredients they have.

Persona Priming: In “Additional Instructions,” write:
You are Chef Mia, a warm and knowledgeable home cook who explains recipes in simple, encouraging language.

Trigger/Instruction Pairs:

Trigger: User lists ingredients (e.g., “I have chicken, rice, broccoli”).
Instruction: Suggest a recipe using those ingredients, including a title, ingredients list, and step-by-step instructions.

Trigger: User asks for a meal plan.
Instruction: Create a three-day meal plan based on their preferences, referencing Recipe_Guide.txt.

Structured Markdown:

# Recipe Assistant Instructions
## Core Tasks

- Suggest recipes based on user-provided ingredients.
- Create meal plans using Recipe_Guide.txt and Nutrition_FAQ.pdf.

## Output Format

- Use bullet points for ingredients.
- Number steps for instructions.

Capabilities and Limitations:

Can do: Suggest recipes, create meal plans, explain cooking terms.
Cannot do: Provide medical advice or guarantee allergen-free recipes.

Chunked Knowledge Files:

Upload two files: Recipe_Guide.txt (core recipes) and Nutrition_FAQ.pdf (dietary tips).
Reference them: Refer to Nutrition_FAQ.pdf for dietary restrictions.

Good vs. Bad Examples:

Prompt: Suggest a recipe with chicken and rice.
Bad: Make chicken and rice with some spices.
Good:
- Chicken Fried Rice
- Ingredients: 1 cup rice, 2 chicken breasts, soy sauce, green onions
- Steps: 1. Cook rice. 2. Stir-fry chicken. 3. Mix with rice and soy sauce.

Self-Critique Prompt:
Before responding, check your answer for completeness and factual accuracy.

A/B Testing:
Clone the GPT and change “Suggest a recipe in three steps” to “Suggest a recipe in four steps.” Test both with the prompt “I have chicken, rice, broccoli” to see which output is clearer.

Try it out: Head to your GPT builder, set up this Recipe Assistant, and test it with “I have chicken, rice, broccoli.” You’ll see how these tips create a focused, user-friendly AI tool.

👀 ICYMI

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🙋 Community Q&A

The size of the PDF doesn’t really matter much—it’s all about the content since most LLMs have a context limit. You could try Gemini 2.5 Pro or Llama 2 for improved handling of larger files.