LLMs can produce authoritative-sounding answers that are completely wrong because they lack access to company data, policies, product catalogs, and recent metrics. Many companies focus on selecting an LLM while neglecting the infrastructure needed for reliable production performance. A significant share of generative AI projects may be abandoned after proof of concept due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. Retrieval-augmented generation connects models to external knowledge bases to improve accuracy, but it requires careful engineering such as indexing choices, document chunking, relevance scoring, and handling cases where retrieval yields nothing useful. Failures can still occur when source documents are poorly structured or user queries are too vague.
"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."
"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."
Read at Entrepreneur
Unable to calculate read time
Collection
[
|
...
]