A Large Language Model (LLM) is an AI system trained on massive amounts of text data that can understand and generate human-like responses. Think of it as a highly capable assistant that has read millions of books, articles, and websites, and can now help you with writing, analysis, problem-solving, and decision-making tasks.

Unlike traditional software that follows fixed rules, LLMs learn patterns from data and can handle situations they've never seen before. For example, you could give an LLM a customer email asking about a refund policy, and it can understand the intent, check relevant information, and draft an appropriate response, even if the specific scenario is unique.

LLMs power tools like ChatGPT, Claude, and many business AI applications. They're particularly valuable in business operations because they can process unstructured data (emails, contracts, invoices, reports) and turn it into actionable insights or completed tasks.

They can extract information from documents, categorize items, detect anomalies, draft responses, and even make recommendations based on complex criteria.

For business leaders, the key insight is that LLMs enable automation of knowledge work that previously required human judgment. Tasks like reviewing contracts for specific clauses, matching invoices to purchase orders with tolerance for variations, or responding to customer inquiries with context-aware answers are now automatable at scale.

Frequently Asked Questions

How is an LLM different from regular automation tools like macros or RPA?

Traditional automation follows exact rules. LLMs understand meaning. If you program a macro to route emails with "Approved" in the subject line, it won't catch "Looks good," "Go ahead," or "Yes, proceed."

An LLM understands these all mean approval, just like a person would. This is why LLMs can handle messy, real-world business data that would break traditional automation.

What can LLMs actually do in a business context?

LLMs excel at tasks involving reading, writing, and reasoning. They can extract data from emails and documents, categorize items (like expense reports or support tickets), draft communications, summarize long documents, compare information across multiple sources, and flag exceptions.

For instance, an LLM could read supplier invoices in different formats, extract the key details, match them to purchase orders, identify discrepancies, and route items appropriately based on your approval rules.

Are LLMs accurate enough for finance and accounting work?

LLMs can be highly accurate when properly deployed, but they're not perfect. The key is structuring processes so the LLM handles what it's good at (reading, extracting, categorizing) while flagging edge cases for human review.

For instance, you wouldn't let an LLM blindly approve all invoices, but you could let it process straightforward matches automatically while sending complex cases or large amounts to a human.

Zamp addresses this through its "Needs Attention" status, which automatically flags items where the agent detects uncertainty or complexity. Combined with approval rules you define in the Knowledge Base (like "flag anything over $10,000" or "send to manager if vendor is new"), you maintain control while still gaining efficiency. Activity logs record every decision, so you have a complete audit trail.

How much does it cost to use LLMs for business automation?

LLM costs have dropped dramatically. Most are priced per million "tokens" (roughly 750,000 words), often costing just a few dollars per million tokens. For context, processing 1,000 invoices might cost $5 to $20, depending on complexity.

The bigger cost consideration is often integration and setup, not the LLM usage itself. Many business automation platforms, including Zamp, bundle LLM costs into their pricing, so you pay per process or outcome rather than tracking tokens.

What's the difference between an LLM and an AI agent?

An LLM is the "brain," while an AI agent is the "employee." The LLM provides the intelligence (understanding language, reasoning, generating responses), and the agent is the system that uses that intelligence to complete specific jobs.

Think of it this way: an LLM is like having knowledge and reasoning ability, while an agent is like having a role, responsibilities, and the ability to take action. A Zamp digital employee is an AI agent that uses an LLM as its core intelligence, but also has connections to your systems, defined processes, approval workflows, and structured boundaries.

Can LLMs integrate with our existing systems like NetSuite or SAP?

LLMs themselves don't integrate with systems directly. They need to be part of a broader solution that connects to your tools. The LLM handles the "thinking" part (reading an email, determining what needs to happen), while integration code handles the "doing" part (pulling data from SAP, updating NetSuite, sending a Slack message). The good news is that modern automation platforms handle these integrations for you.

Zamp solves for this by providing pre-built connections to common business systems (ERPs, email, Slack, procurement tools, databases). You define the process in plain language in the Knowledge Base, and Zamp's digital employees use LLMs to understand instructions while connecting to your actual systems to take action. You don't need to build or maintain these integrations yourself.

What are the risks of using LLMs for business-critical processes?

The main risks are accuracy issues (LLMs sometimes generate plausible but incorrect information), lack of transparency (understanding why the LLM made a decision), and consistency (LLMs might handle similar situations slightly differently).

There's also a risk of over-reliance, where people stop reviewing outputs they should be monitoring. The key is treating LLMs as capable assistants, not infallible oracles, and building appropriate checkpoints and oversight into your processes.

Zamp addresses this with multiple safeguards.

Activity logs capture every action and decision, giving you full transparency. The Knowledge Base lets you define clear boundaries and rules, so agents don't operate beyond their defined scope. Approval checkpoints ensure humans review critical decisions. The dashboard shows process health, so you can spot issues quickly. And the Needs Attention status means the agent raises its hand when it encounters something uncertain rather than guessing.

How long does it take to train an LLM on our company's data?

Most businesses don't need to train an LLM from scratch, which is good because that would cost millions of dollars and months of time. Instead, you use existing LLMs (which already understand language and business concepts) and give them context about your specific operations.

This is done through "prompt engineering" (giving good instructions) and providing relevant information when needed. For example, you might give the LLM your vendor list, approval matrix, and payment terms, so it has the context it needs to process invoices correctly. This takes days or weeks, not months.