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Understanding Training Data & Model Behavior

To use AI effectively at an intermediate level, you need to understand how training data shapes model behavior. This knowledge helps you predict how models will respond and work around their limitations.

The Training Data Foundation​

What Goes Into Training​

Modern AI models are trained on diverse datasets including:

Text Sources:

  • Web pages and articles
  • Books and academic papers
  • Code repositories (GitHub, Stack Overflow)
  • Reference materials (Wikipedia, encyclopedias)
  • Social media and forum discussions
  • News articles and journalism

Quality vs Quantity:

  • Quantity: More data generally improves knowledge breadth
  • Quality: High-quality data improves reasoning and accuracy
  • Diversity: Varied sources prevent overfitting to specific styles
  • Filtering: Removing low-quality or harmful content

How Training Data Affects Output​

Knowledge Boundaries:

  • Models know what was in their training data
  • Information cutoff dates limit current knowledge
  • Popular topics get more representation than niche subjects
  • Regional or cultural biases reflect data sources

Style and Tone:

  • Formal academic writing vs casual conversation
  • Technical documentation vs creative content
  • Professional communication vs social media style

Bias in AI Systems​

Types of Bias​

Representation Bias:

  • Certain groups underrepresented in training data
  • Geographic or cultural skewing
  • Language and dialect preferences

Confirmation Bias:

  • Reinforcing existing societal biases
  • Perpetuating stereotypes present in data
  • Historical inequities reflected in outputs

Selection Bias:

  • What data was included vs excluded
  • Demographic composition of data creators
  • Platform-specific content characteristics

Recognizing Bias in Practice​

Watch for these patterns:

  • Assumptions about default demographics
  • Cultural preferences in recommendations
  • Professional or role stereotypes
  • Geographic assumptions (US-centric responses)
  • Language formality preferences

Example:

Prompt: "Write a job description for a nurse"
Biased response: Uses "she/her" pronouns exclusively
Better response: Uses gender-neutral language or mixed pronouns

Model Capabilities by Training Focus​

Code-Focused Models​

Training emphasis:

  • Programming languages and syntax
  • Software development patterns
  • Technical documentation
  • Error handling and debugging

Best at:

  • Writing functional code
  • Explaining programming concepts
  • Debugging and optimization
  • Architecture recommendations

Limitations:

  • May lack domain knowledge outside tech
  • Can struggle with business context
  • Sometimes over-engineers simple solutions

General Purpose Models​

Training emphasis:

  • Broad knowledge across domains
  • Conversational ability
  • Multiple task types
  • Balanced capabilities

Best at:

  • General knowledge questions
  • Creative writing and ideation
  • Multi-domain discussions
  • Flexible task handling

Limitations:

  • May lack deep expertise in specific fields
  • Can be less precise for technical tasks
  • Sometimes too general for specialized needs

Domain-Specific Models​

Training emphasis:

  • Specialized knowledge (medical, legal, scientific)
  • Domain-specific terminology
  • Professional standards and practices
  • Industry-specific problems

Best at:

  • Expert-level domain knowledge
  • Professional communication styles
  • Specialized problem-solving
  • Compliance with industry standards

Limitations:

  • Narrow scope outside their domain
  • May struggle with general tasks
  • Can be overly formal or technical

Data Quality Issues​

Common Problems​

Hallucination Sources:

  • Conflicting information in training data
  • Gaps filled with plausible-sounding but false information
  • Pattern matching rather than true understanding
  • Confidence in uncertain predictions

Inconsistency Causes:

  • Multiple sources with different perspectives
  • Evolving information over time
  • Context-dependent truth
  • Ambiguous or incomplete data

Outdated Information:

  • Training data cutoff dates
  • Rapidly changing fields (technology, current events)
  • Historical bias in older sources
  • Temporal context confusion

Mitigating Data Quality Issues​

Verification Strategies:

  1. Cross-reference important facts

    • Check multiple sources
    • Verify with authoritative references
    • Look for consensus across sources

  2. Understand model limitations

    • Know the training cutoff date
    • Recognize knowledge gaps
    • Expect uncertainty in edge cases

  3. Provide additional context

    • Include relevant background information
    • Specify current date and context
    • Clarify ambiguous terms

Working with Model Knowledge​

Leveraging Strengths​

Pattern Recognition:

  • Use models to identify trends in data
  • Extract insights from large amounts of information
  • Find connections across different domains

Synthesis and Analysis:

  • Combine information from multiple sources
  • Break down complex problems
  • Generate multiple perspectives on issues

Creative Applications:

  • Brainstorming and ideation
  • Alternative approaches to problems
  • Connecting disparate concepts

Compensating for Weaknesses​

For Factual Accuracy:

  • Treat model output as drafts, not final answers
  • Verify claims with authoritative sources
  • Use models for structure and reasoning, not facts

For Bias:

  • Explicitly request diverse perspectives
  • Ask models to consider alternative viewpoints
  • Review outputs for unconscious assumptions

For Currency:

  • Provide recent information in your prompts
  • Use models for analysis, not current facts
  • Combine AI with real-time data sources

Advanced Prompting for Better Results​

Context-Aware Prompting​

Provide domain context:

You are a cybersecurity expert reviewing code for a financial services company. 
Analyze this authentication function for security vulnerabilities, considering:
- PCI DSS compliance requirements
- OWASP security standards
- Financial industry regulations

Specify perspective:

From the perspective of a small business owner with limited technical expertise, 
explain the trade-offs between cloud and on-premise solutions for customer data storage.

Bias-Aware Prompting​

Request multiple perspectives:

Analyze the impact of remote work policies from three perspectives:
1. Employee well-being and work-life balance
2. Organizational productivity and culture
3. Economic implications for cities and local businesses

Challenge assumptions:

List assumptions commonly made about [topic] and provide evidence that challenges each assumption.

Understanding Model Updates​

Why Models Get Retrained​

Data Updates:

  • New information and knowledge
  • Bias reduction efforts
  • Quality improvements
  • Expanded language support

Capability Improvements:

  • Better reasoning abilities
  • Enhanced safety measures
  • Reduced hallucination rates
  • Improved instruction following

Safety Enhancements:

  • Harmful content filtering
  • Bias mitigation
  • Privacy protection
  • Misinformation reduction

Adapting to Model Changes​

Test your workflows:

  • Run existing prompts with new model versions
  • Compare outputs for consistency
  • Adjust prompting strategies as needed

Stay informed:

  • Follow model release notes
  • Join developer communities
  • Monitor performance on your key use cases

Practical Exercise​

Test model knowledge boundaries and biases:

  1. Knowledge Boundary Test:

    What happened in [specific field] after [model's cutoff date]?

  2. Bias Recognition Test:

    Describe a typical day for a CEO, nurse, and software engineer.

    (Look for gender, cultural, or professional stereotypes)

  3. Source Recognition Test:

    How confident are you about [obscure fact]? What sources would you recommend to verify this?

Key Takeaways​

  • Training data fundamentally shapes what models know and how they respond
  • Bias is present in all AI systems and should be actively considered
  • Model specialization affects performance on different types of tasks
  • Data quality issues require verification and critical evaluation
  • Understanding limitations helps you use AI more effectively
  • Context-aware prompting can improve output quality and reduce bias

What's Next?​

Understanding how training shapes model behavior prepares you for the next crucial skill: fine-tuning and customization. Let's explore how to adapt models for specific use cases.