Unlock Better LLM Results with Your Data

Learn to structure and govern enterprise data for reliable LLM outputs.

Most executives first saw large‑language models (LLMs) in a slick demo: ask a question, get a perfect answer, and save a bundle on support costs. That was the early magic of ChatGPT at launch.

That illusion shatters the moment the same model is pointed at the company’s own documents. Instead of crisp answers, you get hallucinations—responses that sound fluent yet rest on flimsy statistical footing—along with privacy worries and the sinking realization that the model doesn’t “know” your business at all.

A hallucination arises when the model’s next‑token probabilities fail to reach a clear consensus, often because it has too little domain context or conflicting training signals. In practical terms, the model fills gaps with the most plausible-sounding words, producing confident prose unsupported by evidence.

Today, the instinctive fix is to switch on the chatbot’s “memory.” Remembering past chats sounds helpful, yet a model that memorizes outdated or wrong material only serves bad information faster. True reliability comes from something far less glamorous: a dependable flow of fresh, well-labeled data—or in the case of ChatGPT, well-curated memory.

Last week I outlined the concept of EnterpriseGPT—a framework that mixes up‑to‑the‑minute internal data with privately trained models and the best public frontier models while respecting data sovereignty. Building that vision starts with the same lesson we learned in the cloud era: fix the plumbing first.

Why Today’s Data Pipelines Break

Think of your data pipeline as a factory supply chain. In a proof of concept, you feed the factory ten pristine widgets (hand‑picked PDFs) and everything looks fine. Go live and ten pristine widgets become a million dented, mislabeled ones—scanned contracts, half‑filled web forms, and slide decks with no metadata. The machines jam.

Gartner labels this problem the “unstructured‑data quality gap.” Analysts warn that most organizations lack even basic processes to reject unreadable files or flag missing metadata.

Research from EyeLevel AI shows model accuracy sliding by twelve points when document counts pass 100,000, because retrievers can’t find the right passages. When the factory jams, the chatbot hallucinates, trust evaporates, and the clean‑up bill arrives.

Why Memory Needs Governance

Chatbots can now “remember” user details, but memory is helpful only when it follows the same rules as every other corporate system. OpenAI and Google let you toggle memory settings and delete stored data, the responsibility for compliance ultimately remains with you. If a customer’s tax file number sneaks in, the chatbot will happily repeat it until someone notices.

Just as email archives have retention schedules and legal holds, chatbot memory needs classification labels, automatic redaction, and regular audits. Treat it otherwise and it becomes the quickest route to a data‑leak headline.

Vendors Differ—And That Creates New Lock‑In

Behind the marketing gloss, each vendor handles memory and data very differently. ChatGPT Enterprise turns memory off by default, leaving it to administrators. Google Gemini keeps “Saved Info” that admins can purge, while Anthropic Claude forgets everything after each session, forcing companies to add their own storage. Amazon Bedrock keeps chat history for as little as one day or as long as a year—but charges you for every stored token.

Pick a platform and you aren’t just choosing a model; you’re also choosing a retention policy, an egress path, and a potential exit fee for moving your embedded knowledge elsewhere. This means that vendor lock-in now occurs at the data layer, not just at the model level.

Retrieval‑Augmented Generation (RAG): A Practical Fix

Enterprises are growing the capabilities of their system by building a retrieval‑augmented generation pipeline, or RAG for short. RAG works like a live briefing room: every time the model gets a question, it first fetches the newest, most relevant snippets from a searchable index of your documents and then formulates an answer.

A production‑grade RAG pipeline has four moving parts:

• Ingestion – automated crawlers or webhooks scoop up changes from intranets, websites, SaaS apps, and regulatory feeds the moment they appear. Check out this week’s AI Toolbox for tools that do this.
• Indexing – a processing stage that slices each document into bite‑sized chunks, adds labels (author, date, sensitivity), and stores them in a vector database (like Milvus or MongoDB Atlas).
• Each content chunk is converted into a dense vector using an embedding model (e.g., OpenAI, Cohere, Hugging Face). These vectors encode semantic meaning—capturing the relationships between concepts rather than just keywords. For instance, "revenue forecast" and "projected income" are placed near each other in vector space because they convey similar ideas. These vectors are stored in a vector database to support efficient similarity search.
• Governance – policy engines quarantine or redact material that violates compliance rules before it ever reaches the index.
• Evaluation – nightly tests measure how often the model fetches the right chunk, how quickly it answers, and whether hallucinations creep back in. Tools like AutoRAG and Arize Phoenix tune the settings automatically.

Microsoft’s reference RAG architecture shows why the approach works: the search index updates offline every few minutes, while the chatbot simply “checks the index” in real-time. The result is answers based on today’s data, not last quarter’s.

How to Evaluate Whether Your LLM Is Telling the Truth

Before any rollout moves beyond a pilot, leaders need a scoreboard that shows—not guesses—how well the model performs. The academic community already tracks dozens of public benchmarks, but those tests rarely resemble a company’s day‑to‑day questions. Practical evaluation starts with a private “challenge set” of a few hundred real queries drawn from support logs, sales chats, or policy manuals. Each answer is graded by subject‑matter experts so the team has a gold standard.

Most teams focus on four practical metrics. Answer relevance checks whether the response actually addresses the question. Hallucination rate counts factual errors or invented citations—an early sign the model is filling knowledge gaps with guesswork. Retrieval hit rate measures how often the correct document snippet makes it into the model’s context window, while latency shows whether users will tolerate the wait. Tools such as the open‑source Evals framework and commercial open source dashboards like Arize Phoenix compute these numbers automatically. If relevance slips below an agreed threshold—often 85%—the pipeline triggers a data refresh or model update before anyone notices.

With a repeatable evaluation loop in place, business leaders can ask a weekly question that matters: “Is the assistant still passing our accuracy threshold?” If the answer is no, the fix is data first, model second.

Why Humans Still Matter: Human‑in‑the‑Loop and RLHF

Automated metrics keep score, but people still write the rulebook. Human‑in‑the‑loop (HITL) means routing a sample of model answers to experts—support agents, compliance lawyers, and product specialists—who mark them up for accuracy and tone. Their feedback is fed back into the system so the retrieval layer can learn which chunks truly answer which questions (When you click the thumbs up or thumbs down in ChatGPT, this is what you are doing for OpenAI).

When that feedback is aggregated and used to steer model weights the process is called reinforcement learning from human feedback (RLHF). Think of it as fitting the model not just to facts but to your organization’s definition of “a good answer.” Each round of RLHF makes the assistant more aligned with company policy, brand voice, and risk appetite, closing the gap that raw metrics alone can’t capture.

For most enterprises, the path starts simple: review 5 percent of daily chats, log corrections in a ticket queue, and retrain the model monthly. Over time, the loop tightens—feedback is captured in real-time, high‑risk queries are flagged for immediate human review, and RLHF fine‑tunes the assistant every sprint. The result is a system that improves with use, just like a seasoned employee gathering experience.

How Business Leaders Should Move Forward with AI

Start by measuring how fast data moves from its source to the model and how often answers are wrong. If you can’t see those numbers, you’re flying blind. Next, publish plain‑language rules that state what the model may remember and for how long, and make every vendor comply. Finally, run a pilot RAG project on an easily defined data set—product manuals or HR policies—to prove the concept, measure cost, and spot compliance gaps while the stakes are low.

Companies that invest in clean data plumbing, governed memory, and a RAG pipeline will own assistants that inform decisions with confidence. Those who chase shiny demos without fixing the pipes will discover that AI can amplify confusion just as quickly as it promises insight.