A Practical Guide to Building Better AI (ML) Systems

Building successful AI applications is complex. After working with hundreds of AI projects, certain patterns of failure emerge repeatedly of AI development.

They aren’t just theoretical pitfalls; they’re real mistakes that derail projects, waste resources, and create systems that fail when they matter most. More importantly, each pattern has a corresponding “penance” — a practical solution that transforms these failures into robust, reliable AI systems.

Pattern 1: Demanding 100% Accuracy

Building AI products with the expectation that they must be 100% accurate, especially in high-stakes domains like space or medical.

Remember, it is a probabilistic system, no a deterministic software!. So no expect these AI systems to be more accurate than a human to be.

The penance:

• The goal is not to replace human judgment but to save users time
• Make the AI’s work visible, allowing users to verify output and act as the final authority
• Position the AI as a time-saving assistant, not an infallible oracle

Pattern 2: Too Much Autonomy

Giving an agent a large, complex, closed-form task and expecting a perfect result without supervision.

The penance:

• Break down large tasks into smaller steps
• Create opportunities for the agent to ask questions
• Show intermediate steps to the user

Pattern 3: Irresponsible Context Stuffing

With ever-larger context windows, simply “shoving it all into the prompt” and performing RAG over the full content degrades model accuracy as critical information gets lost in noise, while increasing cost and latency without corresponding performance improvements.

The penance:

• Don’t brute-force the context window
• Tier context to ensure only vital information makes it into the prompt
• Use templates that prioritize clarity over maximum capacity

Pattern 4: Starting with Multi-Agent complexity

Jumping directly to complex, orchestrated multi-agent frameworks for problems that may not require them.

The penance:

• Start simple
• Learn the limitations before adding complexity
• Only when the pain of the simple approach becomes clear add complexity incrementally

Pattern 5: Treating RAG as a Black Box

When the system fails, this RAG black-box approach makes it nearly impossible to diagnose whether the fault lies in data ingestion, retrieval, or generation.

The penance:

• Understand the individual pieces—indexing, query creation, retrieval, and generation
• If the answer is wrong, inspect retrieved context to identify the problem source
• Build in logging and metrics for each pipeline stage

Pattern 6: Overloading the prompt

Asking a model to extract multiple unrelated pieces of information or perform several judgments simultaneously often leads to hallucinations, missing tasks, or low-quality output as attention becomes divided.

The penance:

• Use several smaller, focused prompts instead of one large prompt
• Trade small latency increases for large accuracy gains
• Instead of “find all hallucinations,” ask “Is this specific statement supported? True or False”

Pattern 7: Chasinng frameworks and logos

Over-indexing on third-party frameworks too early leads to vendor lock-in, opaque stack traces, and technical debt when the ecosystem inevitably shifts.

The penance:

• Build core components yourself initially. LLM call is just an HTTP request
• Simple, purpose-built tools often outperform generic platforms
• Pick solutions that solve clear problems and anticipate that you’ll still need to write custom code

Conclusion

Think of your LLM as a “super intelligent AI intern.”

This intern is brilliant but he get distracted easily and wake up every morning (with every API call) with no memory of the past. He wants to help but need clear guidance, focused tasks, and regular check-ins.

This perspective shifts the focus from finding the perfect tool to designing robust, debuggable systems that work with the model’s nature, not against it.

When you approach AI development with this mindset, you naturally avoid the seven deadly Patterns:

• You wouldn’t expect a human intern to be 100% accurate without oversight
• You wouldn’t give them a complex project without breaking it into steps
• You wouldn’t overwhelm them with irrelevant information
• You wouldn’t throw them into a complex team structure on day one
• You’d want to understand their thought process when they make mistakes
• You’d give them one clear task at a time
• You’d build relationships and processes rather than relying solely on external tools

The future of AI development belongs to those who understand that the most sophisticated technology requires the most thoughtful human design.

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These principles form the foundation of building production-ready AI systems that deliver consistent value while avoiding common pitfalls that derail projects.

What are your thoughts? I’d love to hear about your experiences and challenges. Feel free to reach out at cguzwork@cguz.org.