> For the complete documentation index, see [llms.txt](https://jamiewen00.gitbook.io/ai-engineering-handbook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jamiewen00.gitbook.io/ai-engineering-handbook/building-ai-products/anti-patterns.md).
# Anti Patterns
Common failures that waste time and money. Extracted from [AI Engineering Anti-patterns](https://youtu.be/tng20xPUpWg) by Chip Huyen
#### ⛔️ Using GenAI Unnecessarily
Teams adopt generative AI for status, not solutions. Companies want bragging rights: "we use AI."
Example: Energy startup built GenAI for optimisation. A simple algorithm checking cheap hours works better.
#### ⛔️ Premature Complexity
Teams rush to complex solutions first. "I must climb that mountain" mentality. Some common mistakes
* Vector databases before basics
* Agent frameworks too early
* Fine-tuning before prompting
#### ⛔️ Bad Product or Bad AI? or Both?
> 💡 Technical excellence ≠ product success.
Meeting summaries example:
* ❌ Summary length (3 vs 5 sentences)
* ✅ Action items from meetings
LinkedIn job recommendations example:
* ❌ Match accuracy scores
* ✅ Skill gap identification
Tax chatbot example:
* ❌ Users don't know what to ask
* ✅ Suggest relevant FAQs
#### ⛔️ Not Understanding Users
User experience means understanding human behaviour.
> 💡 Users want help, not judgment.
#### ⛔️ Overcomplicating RAG Systems
Teams rush to vector databases.
* **Start here**: Keyword search, clean data, smart chunking
* **Data prep matters**: Add context, include titles and keywords
#### ⛔️ Using Agent Frameworks too early
Agent frameworks create hidden complexity.
* ❌ Framework bugs become your bugs
* ❌ Starter prompts contain typos
* ❌ Performance fluctuates with framework changes
* ❌ Hard to diagnose root causes
[Direct API calls for simple tasks](https://www.anthropic.com/research/building-effective-agents) beat frameworks.
1. Build core logic with direct API calls
2. Measure performance with 100+ test cases
3. Identify specific bottlenecks frameworks solve
4. Only then evaluate if framework complexity is worth it
#### ⛔️ Lack of Human Evaluation
AI judges help but have limits. Add human evaluation.
Limitations:
* Models change
* Hard to track progress
* Teams lose touch with users
* Tools score differently
#### ⛔️ Not Localising Failures
Multi-step AI needs failure isolation.
* ❌ Resume extraction fails. Which step? PDF to text? Text to data?
* ✅ Debug each step separately.
#### ⛔️ Overindexing on Early Success
Weekend demos don't predict launch timelines. Demo = 20% of total work.
Production needs:
* Error handling
* User testing
* Performance tuning
* Infrastructure
* Security
* Monitoring
* ...and more
#### ⛔️ Giving Up Too Early
Teams quit after first failures. Find root causes. Fix them. Common causes:
* Bad product design
* No user research
* Poor evaluation
#### Issues
* Teams [couldn't update without breaking changes](https://shashankguda.medium.com/challenges-criticisms-of-langchain-b26afcef94e7) on 0.0.\* versions
* One team built POC with LangChain, [couldn't update without major code changes](https://content.techgig.com/technology/why-developers-are-not-using-langchain/articleshow/118705898.cms)
* [Syntax errors in framework scripts](https://www.reddit.com/r/AutoGPT/comments/13gpirj/autogpt_seems_nearly_unusable/) cause unpredictable failures
* [Direct API calls outperform LangChain](https://blogs.adityabh.is-a.dev/posts/langchain-vs-openai-simplicity-vs-scalability/) by 25% on average
* Frameworks add [layers that make systems slower](https://content.techgig.com/technology/why-developers-are-not-using-langchain/articleshow/118705898.cms) and harder to manage
* [Multi-step task failures](https://arxiv.org/abs/2505.00212) due to complex state handling
* When frameworks fail, you debug [framework code instead of your logic](https://medium.com/@ken_lin/why-smart-developers-are-moving-away-from-langchain-9ee97d988741)
* Framework updates modify [starter prompts without notice](https://www.taivo.ai/__why-autogpt-fails-and-how-to-fix-it/), breaking performance
* [Excessive abstraction obscures](https://medium.com/@ken_lin/why-smart-developers-are-moving-away-from-langchain-9ee97d988741) rather than simplifies
* [CrewAI execution loops](https://github.com/crewAIInc/crewAI/issues/737)
* [CrewAI production challenges](https://timothy-urista.medium.com/crew-ai-and-the-challenges-of-cloud-agentic-frameworks-6fd49cc5d909)
* [Why developers abandon LangChain](https://medium.com/@ken_lin/why-smart-developers-are-moving-away-from-langchain-9ee97d988741)
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