The AI bill is coming due. Businesses are learning tokens aren’t free
Companies rushed to put AI tools in employees’ hands. Now surprise usage costs are forcing them to ask what all those prompts are actually worth.
InfoWorld AI·
I’ve seen a lot of promising AI prototypes fall apart after launch. And it’s rarely because the model was bad. More often, the problem starts much earlier; teams treat the data layer like something they can figure out later. They’ll spend weeks fine-tuning prompts, testing models and debating evaluation scores, then throw together the retrieval pipeline over a weekend and move on. At first, everything looks great in demos. But a few months later, the system gives outdated answers; the embeddings no longer match the source documents, and nobody fully understands what changed. What started as an impressive prototype slowly becomes difficult to trust in production. The teams that avoid this tend to realize one thing early: Embedding pipelines are fundamentally a data engineering problem, not an entirely new AI discipline. It’s still ETL (Extract, Load, Transform) at its core, but with embeddings and vector stores as the destination instead of a warehouse. Once you start looking at it that
Read full articleCompanies rushed to put AI tools in employees’ hands. Now surprise usage costs are forcing them to ask what all those prompts are actually worth.
Modern AI applications rely on understanding meaning rather than matching keywords. As large language models, semantic search, and RAG systems have become mainstream, vector databases have emerged as critical infrastructure for storing and retrieving high-dimensional embeddings at scale. Choosing the right vector database can have a major impact on performance, scalability, cost, and developer experience. […] The post Choosing the Right Vector Database for RAG and AI Applications appeared first on Analytics Vidhya.
Enterprise Document Intelligence [Vol. 1 #2] Why the same vector search that handles synonyms and paraphrase silently fails on negation, exact identifiers, and your company’s acronyms, and what to use when it does. The post Embeddings Aren’t Magic: The Predictable Failure Modes of RAG Retrieval appeared first on Towards Data Science.
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A colleague told me something recently that I keep thinking about. She said, unprompted, that she appreciated seeing both sides of my AI conversations. Not just the output. The full thread. My prompts, the AI’s responses, the back and forth, the dead ends, the iterations. She said it made her trust me more. This piece […]