Getting Better Results

Beyond writing great prompts, there are many strategies to get better, more accurate, and more useful results from ai.KMITL. This guide covers advanced techniques and best practices.

Understanding AI Behavior

How AI Models Work

AI models:

• Predict text based on patterns in training data
• Don't "know" things like humans do
• Can make mistakes or "hallucinate"
• Benefit from context and clear instructions
• Improve with feedback and iteration

Key Insight

AI is a tool that amplifies your thinking - it works best when you guide it clearly and verify its outputs.

Iterative Refinement

The Iteration Process

Don't expect perfect answers on the first try. Instead:

1. Start with a good prompt
2. Review the response
3. Refine your request
4. Build on what works
5. Repeat until satisfied

Example Iteration

First attempt:

You: "Explain machine learning"

AI: [Generic explanation]

Second attempt:

You: "That's helpful, but can you focus on practical applications
rather than technical details?"

AI: [Better, more practical explanation]

Third attempt:

You: "Perfect! Now give me 3 real-world examples of machine
learning in everyday life"

AI: [Exactly what you needed]

Providing Better Context

What to Include

For technical questions:

• Your skill level (beginner/intermediate/advanced)
• What you've already tried
• Specific error messages
• Your environment (Python 3.10, Windows, etc.)
• What you want to accomplish

For creative tasks:

• Target audience
• Desired tone and style
• Length requirements
• Any specific elements to include/avoid
• Examples of what you like

For learning:

• Your current understanding
• What confuses you specifically
• How you learn best (examples, analogies, step-by-step)
• What you need this knowledge for

Context Example

Weak context:

❌ "Help me with this code"

Strong context:

✅ "I'm a beginner learning Python. I'm trying to read a CSV file
and print each row. I've tried using the csv module but I'm getting
a 'FileNotFoundError'. I'm using Python 3.10 on Windows. Here's my code:
[code]

I want to understand both what's wrong and why."

Breaking Down Complex Problems

The Divide and Conquer Method

Instead of one huge request:

❌ "Build me a complete todo app with authentication, database,
and mobile-responsive design"

Break it into steps:

✅ Step 1: "Let's design the database schema for a todo app.
I need tables for users and todos. What fields should each have?"

✅ Step 2: "Great! Now help me write the SQL to create these tables"

✅ Step 3: "Now let's build the user authentication system..."

Benefits of Breaking Down

• Easier to understand each piece
• Better results per step
• Can adjust direction as you go
• Learn more from the process
• Debug easier when issues arise

Using Web Search Effectively

When to Enable Web Search

Enable for:

• ✅ Current events and news
• ✅ Recent product releases
• ✅ Latest technology versions
• ✅ Real-time data (weather, stocks)
• ✅ Fact-checking specific claims
• ✅ Recent research papers

Don't need for:

• ❌ General knowledge
• ❌ Coding help (unless checking latest syntax)
• ❌ Creative writing
• ❌ Math problems
• ❌ Historical facts

Making Web Search More Accurate

Be specific about time:

✅ "What AI announcements were made this week?"
✅ "Latest iPhone features (2024 model)"
✅ "Current Python version and release date"

Request sources:

✅ "Find recent statistics on climate change and cite your sources"
✅ "What do reputable news sources say about this topic?"

Verifying AI Responses

Always Verify For

• Facts and statistics (especially without web search)
• Code before running in production
• Medical or legal advice (consult professionals!)
• Financial decisions
• Critical academic work

How to Verify

1. Cross-reference with reliable sources
2. Test code in a safe environment
3. Ask for reasoning: "Why did you suggest this?"
4. Use web search to fact-check
5. Consult experts for critical decisions

Critical Information

Never rely solely on AI for medical, legal, financial, or safety-critical decisions. Always consult qualified professionals.

Handling Hallucinations

What are Hallucinations?

When AI generates information that:

• Sounds plausible but is incorrect
• Includes made-up facts, citations, or statistics
• Confidently presents wrong information

How to Reduce Hallucinations

1. Ask for sources:

✅ "Cite your sources for these statistics"
✅ "Where can I verify this information?"

2. Enable web search for facts:

✅ Enable web search when asking factual questions

3. Request uncertainty acknowledgment:

✅ "If you're not certain, please say so"
✅ "What's your confidence level in this answer?"

4. Verify critical information:

✅ Always double-check important facts
✅ Test code before using it

Red Flags

Watch out for:

• 🚩 Overly specific statistics without sources
• 🚩 Quotes from unverifiable sources
• 🚩 Made-up technical terms
• 🚩 Inconsistencies in the response
• 🚩 Claims that sound too perfect

Choosing the Right Model

Task-Model Matching

For deep reasoning: → Claude Opus, GPT-4

For speed: → Gemini Flash, Groq

For long documents: → Gemini Pro (2M tokens)

For balanced performance: → Claude Sonnet

For coding: → Claude Sonnet, GPT-4

See AI Models for complete guide.

When to Switch Models

Switch if:

• Current model seems stuck
• Responses aren't detailed enough
• You need faster responses
• Previous model gave errors
• Task requires specific strength

Don't switch if:

• Current model is working well
• You're mid-conversation and context matters
• Just one response was slightly off (try refining prompt instead)

Using Files Effectively

Preparing Files

For images:

• Use high resolution for text recognition
• Crop to relevant areas
• Ensure good lighting in photos
• Rotate to correct orientation

For PDFs:

• Smaller files (under 10MB) work best
• Text PDFs better than scanned images
• Split large documents if needed
• Remove unnecessary pages

For code:

• Include complete context
• Add comments explaining intent
• Show error messages if debugging
• Include relevant imports/dependencies

File-Related Prompts

Good:

✅ "I've uploaded a diagram. Can you explain what it shows?"
✅ "This PDF contains my essay. Please check grammar and structure."
✅ "Here's a screenshot of an error. What might be wrong?"

Better:

✅ "I've uploaded a flowchart of my algorithm. Please:
1. Verify the logic is correct
2. Suggest optimizations
3. Identify potential edge cases"

Managing Conversations

When to Start a New Chat

Start fresh when:

• Changing topics completely
• Previous conversation was messy
• Context is getting confusing
• You want a "clean slate"
• Hit model's context limit

Continue conversation when:

• Building on previous topic
• Need context from earlier
• In middle of multi-step task
• Model needs previous context

Organizing with Folders

Use folders to:

• Group related conversations
• Find chats easily later
• Keep different topics separate
• Archive completed projects

See Project Folders for details.

Quota Management

Making Messages Count

Strategies:

1. Plan your question before sending
2. Combine related questions into one message
3. Use faster models for simple queries
4. Edit prompts instead of sending corrections
5. Learn from responses to ask better questions

When You're Running Low

• Prioritize important questions
• Use web search judiciously
• Consider BYOK for unlimited access
• Wait for monthly reset (1st of each month)

See Usage Quotas for more.

Advanced Techniques

Chain Prompting

Build complex outputs through stages:

Step 1: "List the main features of a todo app"
→ AI lists features

Step 2: "For feature #3 (user authentication), design the database schema"
→ AI designs schema

Step 3: "Now write the Python code for this schema using SQLAlchemy"
→ AI writes code

Role Assignment

Have AI take specific roles:

✅ "Act as a senior software architect. Review my system design..."
✅ "You're a writing tutor. Help me improve this paragraph..."
✅ "Pretend you're a skeptical scientist. Critique this hypothesis..."

Constraint-Based Prompts

Add specific constraints:

✅ "Explain quantum computing in exactly 3 sentences"
✅ "Write code using only standard library (no external packages)"
✅ "Describe this concept using only everyday analogies"

Few-Shot Learning

Provide examples of what you want:

✅ "Here are 3 examples of the format I want:
Example 1: [your example]
Example 2: [your example]
Example 3: [your example]

Now create one for: [new item]"

Common Pitfalls

1. Not Providing Enough Information

Problem:

❌ "This doesn't work"

Solution:

✅ "This Python function returns None instead of the sum.
Here's the code: [code]. Expected: 15, Got: None"

2. Expecting Perfection First Try

Problem: Getting frustrated when first response isn't perfect

Solution: Treat it as a conversation - refine and iterate

3. Not Verifying Critical Information

Problem: Using generated code or facts without checking

Solution: Always test, verify, and validate important outputs

4. Ignoring Model Limitations

Problem: Expecting AI to know everything or be always right

Solution: Understand limitations, verify facts, use web search

5. Not Learning from Interactions

Problem: Repeating same unclear prompts

Solution: Note what works, refine your approach, build prompt templates

Quality Checklist

Before sending your message, ask:

• [ ] Is my question specific?
• [ ] Did I provide enough context?
• [ ] Is this the right model for the task?
• [ ] Should I enable web search?
• [ ] Have I broken down complex tasks?
• [ ] Am I asking one thing at a time?

After receiving a response:

• [ ] Does this fully answer my question?
• [ ] Do I need to verify this information?
• [ ] Should I ask for clarification?
• [ ] Can I build on this response?
• [ ] Did I learn something new?

Troubleshooting Common Issues

Response is Too Generic

Fix:

• Add more specific details
• Provide examples of what you want
• Specify the format
• Add constraints

Response is Wrong

Fix:

• Enable web search for facts
• Provide correct information and ask to try again
• Try a different model
• Verify your own assumptions

Response is Too Technical

Fix:

• Specify your skill level
• Ask for simpler language
• Request analogies
• Ask "explain like I'm 5"

Response is Too Simple

Fix:

• Ask for more detail
• Request technical depth
• Ask about edge cases
• Request examples

AI Seems Stuck

Fix:

• Rephrase your question
• Start a new conversation
• Try a different model
• Break problem into smaller pieces

Best Practices Summary

Do's ✅

1. Iterate on responses
2. Provide context generously
3. Break down complex tasks
4. Verify critical information
5. Choose appropriate models
6. Organize with folders
7. Learn from each interaction
8. Experiment with techniques

Don'ts ❌

1. Don't expect perfection immediately
2. Don't skip verification
3. Don't use AI for critical decisions alone
4. Don't forget to provide context
5. Don't waste quota on unclear prompts
6. Don't ignore model strengths
7. Don't give up after one try
8. Don't forget AI has limitations

Continuous Improvement

Learn from Each Session

• Note what prompts worked well
• Save useful conversation patterns
• Develop your own prompt templates
• Understand each model's style
• Build a personal playbook

Experiment Regularly

• Try different models for same task
• Test various prompt styles
• Compare results
• Refine your approach
• Share learnings with others

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The Path to Mastery

Getting great results is a skill that improves with practice. Every conversation teaches you something new about effective prompting and AI interaction.

Next Steps

• Practice with Writing Great Prompts
• Explore AI Models capabilities
• Master the Chat Interface
• Check Usage Quotas optimization