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Common Pitfalls in AI Engineering: Learning from Early Adopters

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Common pitfalls when building generative AI applications

Synopsis:

The article explores six common mistakes teams make when building AI applications:

  • Using AI unnecessarily
  • Blaming AI for product issues
  • Starting too complex
  • Overestimating early success
  • Neglecting human evaluation
  • Lacking strategic focus in use cases

Context

As organizations rush to implement AI solutions, understanding common pitfalls becomes increasingly important.

Just as the software industry experienced growing pains in its early days, the AI engineering field is now experiencing maturation challenges.

The stakes are high in 2025 as companies move beyond experimental projects to integrate AI systems into their core business operations.

This transition mirrors the transition that happened with web applications in the early 2000s, when companies moved from experimental websites to mission-critical web applications.

By drawing from public case studies and personal experience, Chip Huyen provides a comprehensive overview of mistakes even experienced teams make.

These insights are especially valuable because they come from real-world implementations rather than theoretical concerns.

Early adopters learned many of these lessons through costly trial and error.

The timing of this article is particularly relevant, as venture capitalists and news organizations have labeled 2025 the year of “AI Agents.”

With the surge of organizations moving from experimental AI projects to production systems, understanding these pitfalls now can help teams avoid repeating the same costly mistakes.

Key Implementation Patterns

The article identifies several critical patterns that often lead to problems:

  1. Technology-First Thinking
  • Using AI because it’s trendy rather than necessary
  • Overlooking simpler, proven solutions
  • Example: Using AI for optimization when simple scheduling would work
    • In one case, a team spent months building an AI system to optimize energy usage, only to discover that basic time-of-use scheduling achieved similar results with far less complexity.
  1. Product-AI Balance
  • Mistaking product issues for AI failures
  • Underestimating UX importance
  • Real-world examples:
    • Meeting summary app users wanting action items, not summaries
    • LinkedIn users seeking helpful rather than merely correct responses
    • Intuit users needing suggested questions rather than a blank open chat text box (as they didn’t know what the AI could do)
  1. Complexity Management
  • Starting with complex frameworks unnecessarily
  • Adding sophisticated features before basics work
  • Introducing unnecessary dependencies too early

These patterns hint at deeper strategic considerations that technical leaders must address.

Strategic Implications

For technical leaders, these patterns suggest several important considerations:

  1. Solution Evaluation
  • Start with problem definition, not technology choice
  • Consider non-AI alternatives first
  • Validate AI necessity before implementation
  1. Development Approach
  • Begin with simple, direct implementations
  • Add complexity only when needed
  • Focus on user experience early
  1. Progress Management
  • Understand the 80/20 rule in AI development
  • Plan for diminishing returns
  • Allocate resources for long-term refinement
  • Remember the 90-90 aphorism/rule:
    • “The first 90 percent of the code accounts for the first 90 percent of the development time. The remaining 10 percent of the code accounts for the other 90 percent of the development time.” - Tom Cargill, Bell Labs
    • This rule becomes even more relevant with non-deterministic AI systems, where the final refinements often require disproportionate effort.

Teams need a clear implementation framework to translate these strategic insights into practical action.

Implementation Framework

For teams building AI applications, the article suggests this approach:

  1. Start with the Problem Definition
  • Clearly articulate the business problem
  • Evaluate multiple potential solutions
  • Consider non-AI approaches first
  • Validate that AI adds genuine value
  1. Build Incrementally
  • Begin with direct, simple implementations
  • Avoid premature optimization
  • Test core functionality before adding complexity
  • Focus on user experience early and often
  1. Implement Proper Evaluation
  • Combine automated and human evaluation
  • Review many examples daily (the article suggests 30-1000 examples per day)
  • Correlate AI judgments with human assessments
  • Use evaluation insights to improve the product

As teams implement these frameworks, several crucial insights emerge that can guide AI engineers.

Key Takeaways for AI Engineers

The article provides several crucial insights for implementation:

  1. Development Strategy
  • Start simple and add complexity gradually
  • Focus on user needs over technical sophistication
  • Plan for the long journey from 80% to 95% success
  • Build evaluation into the development process
  1. Common Challenges
  • API reliability issues (Chip has seen companies that had up to 10% timeout rates)
  • Compliance and security concerns
  • Safety considerations
  • Changing model behaviors
  • Testing complexity with infinite query combinations
  1. Success Metrics
  • Early success doesn’t guarantee easy scaling
  • Progress becomes exponentially harder
  • Resource planning should account for diminishing returns
  • Human evaluation remains crucial

While these lessons come from early adopters, they reflect fundamental challenges in AI system development.

Personal Notes

The article’s insights about the 80/20 rule particularly resonate because this pattern has appeared consistently throughout the evolution of software engineering.

However, it becomes even more pronounced with AI systems because of their non-deterministic nature.

Getting to 80% can feel deceptively easy, leading teams to underestimate the effort required for the final 20%.

In traditional software development, the final effort often focuses on edge cases and optimization.

These challenges are amplified in AI engineering because edge cases can be more numerous, harder to identify, and sometimes impossible to resolve fully.

The emphasis on human evaluation reminds us that while automation is powerful, human judgment remains crucial.

While we have sophisticated automated testing frameworks, there’s no substitute for systematic human review of AI system outputs.

This human review mirrors the evolution of software quality assurance, where automated testing complements but never fully replaces human testing.

Looking Forward: Learning from Early Mistakes

As AI engineering matures, understanding these common pitfalls becomes increasingly valuable.

Teams that internalize these lessons early will:

  • Make better technology choices
  • Build more sustainable solutions
  • Allocate resources more effectively
  • Create better user experiences

The future of AI engineering will likely involve standardized approaches to avoid these common pitfalls, much like how software engineering evolved best practices to avoid common development mistakes.