And the problem might not even be the LLM.
While working with a brand-focused AI application, Ive been learning why building reliable AI systems requires more than just writing a good prompt.
Think about a system that generates marketing copy using a brands knowledge base.
Several things need to work together:
→ Brand context: Does the AI understand the brand, its audience, and its positioning?
→ Retrieval: Did the system find the right information from the uploaded documents?
→ Generation: Did the LLM use that information correctly?
Heres what makes debugging interesting:
If the system retrieves the wrong information, improving the prompt might not fix the problem.
If the right information reaches the LLM but the answer is still wrong, the issue might lie in how the model uses that context.
💡 The key lesson? Before fixing the answer, find out where the system went wrong.
Thats one of the things Im exploring as I go deeper into RAG, evaluation, and building more reliable AI applications.
For those building with LLMs, whats been your biggest challenge: retrieval, hallucinations, or inconsistent outputs?
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