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Key Takeaways LLMs like ChatGPT will generate answers that sound authoritative but are completely wrong, if you don’t monitor it.
You can make AI work for your business by using different prompts and implementing retrieval-augmented generation.
Don’t forget to add guardrails and validation layers to maintain security and avoid touching sensitive data.
This is the gap most companies don’t see coming. They spend months evaluating which large language model (LLM) to use (GPT-4o, Claude, Gemini) and almost no time thinking about the infrastructure that will keep it running reliably. That’s the wrong order of operations. The model is usually the least of your problems.
According to Garner, at least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025, due to poor data quality, inadequate risk controls, escalating costs or unclear business value. The technology works. The engineering around it is where companies fall short.
As an engineer working across MLOps and LLMOps at Axelle AI, with the help of my business partner, who delivered AI projects for banks, I will walk you through what we see every time a company ships an AI system into production.
The first crack: Hallucination
Left alone, an LLM will generate answers that sound authoritative and are completely wrong. It has no access to your data, your policies, your product catalog or last quarter’s numbers. It generates what’s plausible, not what’s true.
The standard fix is retrieval-augmented generation (RAG). As IBM describes it, RAG is an architecture for optimizing AI model performance by connecting it with external knowledge bases. Getting it right takes real engineering: choosing what to index, how to chunk documents, how to score relevance and how to handle cases where nothing retrieved is actually useful.
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