LLM (Large Language Model)

Common LLM uses in contact centres

Customer-facing chatbots and virtual assistants use LLMs to handle queries through chat, email, or messaging. Instead of following scripts, they understand what customers want and generate relevant responses. This works for straightforward queries (order status, password reset, balance enquiry) but struggles with complex or sensitive issues requiring human judgment.

Agent assist tools surface relevant information whilst agents handle interactions. The LLM understands the conversation, searches your knowledge base, and suggests helpful articles, responses, or next steps. This helps agents work faster and more consistently without needing to remember every product detail.

Automated summarisation generates concise summaries of interactions. Instead of agents spending 90 seconds writing wrap-up notes, the LLM produces a summary automatically. This saves time and improves documentation accuracy when it works, or creates useless summaries that agents ignore when it doesn’t.

Draft responses for email and messaging channels. The LLM reads the customer’s message, understands the query, and drafts a response for the agent to review and send. Agents edit rather than write from scratch, which speeds up handling whilst maintaining quality control.

Quality analysis at scale. LLMs can evaluate interactions against quality frameworks, identifying whether agents greeted properly, showed empathy, offered appropriate solutions, and closed correctly. This enables 100% quality coverage rather than sampling 2-5% of interactions.

Intent and sentiment detection by analysing customer messages to understand what they want and how they feel. This drives routing, prioritisation, and helping agents prepare for interactions appropriately.

What makes LLMs powerful

Understanding variation is the key advantage. Customers don’t speak in predictable patterns. They ramble, get sidetracked, phrase things oddly, use colloquialisms, make typos. LLMs handle this naturally where rule-based systems fail.

Context awareness means LLMs track conversation history and reference earlier messages. When a customer says “and what about the other one?” the LLM understands “other one” refers to something mentioned three messages ago. Traditional bots lose context constantly and ask customers to repeat themselves.

Generating natural language responses instead of templated text makes interactions feel conversational rather than robotic. The response isn’t pulled from pre-written scripts – it’s generated to fit the specific situation.

Rapid deployment compared to rule-based systems. Instead of mapping conversation flows and writing scripts for every scenario, you provide context about your business and products. The LLM starts handling queries immediately, improving through feedback rather than requiring complete scenario mapping upfront.

What goes wrong with LLMs

Hallucinations are the most dangerous problem. LLMs generate plausible-sounding responses based on patterns, not facts. When they don’t know something, they often make it up confidently rather than admitting uncertainty. A customer asks about return policy and the LLM invents a policy that sounds reasonable but contradicts your terms.

This makes unsupervised LLM deployment risky. Without proper constraints and fact-checking, they’ll provide wrong information that sounds authoritative, creating bigger problems than they solve.

Outdated information because LLMs are trained on data from specific time periods. Their knowledge has a cutoff date. They don’t automatically know about product launches, policy changes, or current events unless you provide that information separately.

Deploying LLMs without connecting them to current knowledge bases means they’ll answer based on outdated or generic information rather than your specific, current policies.

Inconsistent quality because responses are generated, not scripted. The same question asked twice might get slightly different answers. Usually this is fine, but for regulated industries or compliance-sensitive scenarios, consistency matters more than natural variation.

Prompt dependency means LLM behaviour depends heavily on how you instruct them. Vague prompts produce vague results. Poor prompts create unhelpful or inappropriate responses. Getting prompts right requires expertise and testing, not just switching the technology on.

Cost varies but can be significant at scale. Unlike rule-based bots with fixed costs, LLM pricing often depends on usage volume. Processing thousands of daily interactions through LLM-powered systems costs more than simple automation, though often less than handling everything with humans.

Making LLMs work in contact centres

Ground them in your knowledge by connecting LLMs to accurate, current information about your products, policies, and processes. They should retrieve factual information from your systems rather than generating answers from their training data alone.

This is where knowledge base quality becomes critical. LLMs amplify whatever knowledge exists. Clean, current knowledge produces helpful responses. Outdated or contradictory knowledge gets served up faster but remains wrong.

Set clear boundaries about what LLMs should and shouldn’t handle. Simple queries with factual answers work well. Complex situations requiring judgment, empathy, or authority need human agents. Compliance-sensitive scenarios might need approval before LLM-generated responses go to customers.

Build in human oversight for customer-facing uses. Either agents review LLM responses before sending them, or you monitor interactions closely and intervene when problems surface. Completely autonomous LLMs handling customer contacts is risky without substantial guardrails.

Test thoroughly with real customer queries, edge cases, and adversarial inputs. How does the LLM handle ambiguous questions? What happens when customers get frustrated? Does it stay on topic or get sidetracked? What happens if someone tries to manipulate it into inappropriate responses?

Monitor continuously because LLM behaviour can surprise you. Track what questions it handles well, where it struggles, and what errors it makes. Use this feedback to improve prompts, update knowledge, and refine boundaries.

Provide escape routes so customers can reach humans when LLM interactions fail. Nothing frustrates people more than being trapped in automation that cannot help them and won’t let them speak to someone.

LLMs and agent experience

AI for agents using LLMs should feel like having a knowledgeable colleague who surfaces helpful information instantly. The agent handles the conversation whilst the LLM provides relevant articles, suggests responses, or drafts follow-up emails.

This works when the LLM genuinely helps – providing information faster than agents could find it themselves, suggesting things they might have missed, and reducing repetitive work. It fails when suggestions are irrelevant, responses need heavy editing, or the technology adds steps rather than removing them.

Agents will ignore tools that create more work than they save. LLM-powered assist tools need to deliver value agents can feel, not just capabilities that sound impressive in presentations.

Compliance and regulation

Regulated industries face challenges with LLM deployment. How do you ensure generated responses comply with regulations? How do you audit what an LLM told customers when responses aren’t templated? How do you prove compliance when the system generates unique responses each time?

These aren’t insurmountable problems but require careful design. Some organisations require human approval before LLM responses go to customers. Others restrict LLM use to internal agent assist where compliance concerns are lower. Some build extensive testing and monitoring to catch compliance failures quickly.

The flexibility that makes LLMs powerful also makes compliance harder than with scripted systems. This trade-off needs conscious management, not assumption that technology handles it automatically.

The hype versus reality gap

LLMs are genuinely capable technology that improves contact centre operations when deployed thoughtfully. They’re also surrounded by hype suggesting they’ll replace entire workforces, solve every problem, and require minimal effort to implement successfully.

Reality sits between these extremes. LLMs handle routine queries effectively, freeing humans for complex work. They assist agents by surfacing information quickly. They automate tasks like summarisation that previously consumed agent time.

But they require clean knowledge, good prompts, careful boundaries, and ongoing monitoring. They make mistakes that need catching. They work best augmenting humans, not replacing them entirely. And they’re tools that amplify existing operation quality rather than fixing fundamental problems.

Organisations treating LLMs as magic solutions to operational issues they haven’t bothered fixing will be disappointed. Those using them strategically to handle appropriate use cases whilst maintaining human oversight will see genuine value.

Where this is heading

LLM capability improves rapidly. Systems become more accurate, less prone to hallucinations, and better at admitting uncertainty. Costs decrease as technology matures. Integration with contact centre platforms becomes simpler.

This makes LLM deployment more accessible for smaller operations and more capable for everyone. But the fundamental requirements remain: clean knowledge, clear boundaries, human oversight, and realistic expectations about what technology can and cannot handle.

The question isn’t whether to use LLMs in contact centres. The question is how to use them appropriately for your operation, your customers, and your risk tolerance. Get that right and they transform capability. Get it wrong and they’re expensive technology that creates new problems whilst solving old ones.