Large language model (LLM)

A large language model is a transformer network trained on a very large body of text to predict the next token in a sequence. That single objective, applied at scale, gives the model a usable grasp of grammar, facts, reasoning patterns and code. You interact with it by sending a prompt (instructions plus context) and receiving generated text, either through a provider API such as OpenAI, Anthropic or Google, or by running an open-weight model on your own hardware.

In a product, the LLM is rarely the whole system. It sits behind retrieval, tools, output validation and monitoring. The model supplies language and reasoning; the surrounding software supplies your data, your permissions and your rules. Most production failures come from that surrounding layer being missing, not from the model itself.

An LLM is not a database and not a search engine. It has no reliable memory of your documents unless you put them in the context window, and it will produce fluent text even when it has nothing correct to say (see hallucination). It is also distinct from a classical machine learning model trained on your tabular data for one narrow prediction.

• Founder / CEO: the model is a commodity you rent; the durable asset is the product built around it, and that is what you should be paying for.
• CTO / Head of Engineering: model choice affects cost, latency and lock-in, so architecture should allow swapping providers without a rewrite.
• CFO: LLM usage is metered per token, so spend scales with traffic in a way seat-based software does not.
• Compliance officer: prompts can carry customer data to a third party, which raises DPDP, GDPR and RBI outsourcing questions that need answering before launch.

LLMs exist because rule-based and narrow ML systems could not handle open-ended language: every new intent, document format or phrasing needed new engineering. A general model that can read and write text removes that bottleneck and makes features like summarisation, extraction and conversational interfaces cheap to prototype. The trade-off is non-determinism and cost per call. You gain breadth and speed; you give up the guarantees of deterministic code, which is why evals, grounding and structured outputs become mandatory engineering work rather than nice-to-haves.

• Extracting fields from KYC documents and bank statements in an NBFC lending flow, with exceptions routed to a checker.
• Drafting replies inside a helpdesk so a support agent reviews and sends rather than types from scratch.
• Summarising discharge notes and protocols for clinicians in a hospital network, with the model kept inside the hospital's own infrastructure.
• Answering learner questions grounded in a specific curriculum on an education platform.
• Generating product descriptions and marketplace listings for a retailer's catalogue, checked against attribute data.
• Classifying inbound logistics emails and WhatsApp messages into exceptions, queries and complaints.

ConceptA concept every buyer needs, and a family of technologies your team learns to work with rather than a single skill. Eazyware's LLM applications service covers model selection, integration and the engineering that makes a model safe to ship.

Eazyware service that covers it: LLM Application Development. Starting prices are on the pricing page.