# 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

<mark style="background-color:purple;">User experience</mark> 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)