Evaluate if the LLM provides responses equal to or superior to human or chatbot interactions
If you’re planning to plug a large language model (LLM) into your customer experience pipeline, first ask this: can it outperform what you already have?
We all know the issue with scripted chatbots. They either don’t understand the question or just point you back to the FAQ. If the LLM just mimics that behavior, you haven’t solved a problem, you’ve scaled a bad experience. Same thing goes for undertrained human agents on autopilot, reading what’s on a screen without offering real support. These disappointments don’t earn customer trust, and they certainly don’t generate loyalty.
So, test the LLM. You’ll need to be honest about whether its answers really satisfy the user’s intent, or only sound smart. That’s a key operational difference. Deploy side-by-side comparisons between LLM responses and results from your current systems. Do it with real customer requests, not artificially polished test cases. And don’t just check for relevance. Rate clarity, accuracy, and how quickly the model gets to the point.
The goal is to deliver better results and smarter responses at scale. If the tech doesn’t improve the experience, skip it. If it does, implement it aggressively.
Assess the legal and liability risks of LLM deployment
Deploying any technology at scale introduces some level of risk. With LLMs, that risk shifts from hardware failure or code breaks to content and advice. These systems don’t just display information; they generate language. In the wrong context, that can cause problems fast, especially in sectors where factual accuracy and regulatory compliance are non-negotiable.
If your business operates in law, finance, healthcare, or government, tread carefully. A misstatement from an LLM could lead to bad legal decisions, incorrect diagnoses, or financial losses. These are liabilities that cost money, damage trust, and attract lawsuits. That also applies to less regulated areas. Misleading policy details, inaccurate instructions, or tone-deaf output can quickly spiral into customer disputes or even class actions.
Most leaders are under pressure to innovate. Fair. But that doesn’t mean pushing something live before you understand how it might break. Before deployment, get legal and compliance teams involved. Build safeguards into the LLM’s training data, response structure, and permissions. And define triggers: what happens when the LLM deviates, what gets flagged, and who reviews it. That’s how you use automation without gambling the business.
Determine whether the LLM is cost-effective in the long term
The pricing on general-use LLMs today looks generous. Services like ChatGPT let you prototype and test at relatively low cost. But that’s just surface-level economics. The real cost, the one that matters to your bottom line, starts to show up when you move from testing to long-term operations.
Customized deployment, internal infrastructure, systems integration, model fine-tuning, and ongoing support all introduce additional expense. That means paying for engineering time, data expertise, and multi-layered system monitoring. You may save upfront by reducing headcount in a support center, but you’ll likely redirect those costs toward AI ops and governance functions.
There’s also the issue of sustained pricing. Many of today’s LLM platforms are underwritten by major venture capital or strategic investment. That keeps access affordable, for now. But these platforms will need to generate real revenue eventually. Subscription costs could rise, especially for usage at scale or for custom-trained models.
If you’re making decisions at the executive level, you need to evaluate beyond trial-phase budgets. Run total cost projections over three to five years, with variable models for pricing increases, staffing requirements, and system maintenance. Compare that with the full cost of your current operations. Your goal needs to be to adopt AI in a way that sustains margin, efficiency, and flexibility over time.
Develop a robust maintenance strategy for continuous improvement
Once deployed, an LLM doesn’t run independently forever. It’s learning-based tech that requires oversight, updates, and recalibration. If you’re building a custom solution trained on proprietary data, you’ll also be responsible for making sure it doesn’t produce false, outdated, or irrelevant answers.
Right now, LLMs aren’t reliable at unlearning incorrect information. They don’t forget on command. That means if misinformation slips into your training datasets, or if an output proves problematic, you’ll need a process to detect and correct future responses. Manual retraining, constraints on response types, and flagged feedback loops are key tools. You shouldn’t assume the system will self-correct.
Set up operational procedures focused on lifecycle oversight. That means scheduled audits, content quality reviews, system benchmarking, and detailed retraining plans. These are core to making the system trustworthy and scalable.
Understand that this is an ongoing investment. Business leaders who want to implement AI responsibly need to treat maintenance as a critical line item. A well-maintained LLM will drive ongoing value. One that goes unchecked only gets less accurate, and more risky, over time.
Implement a comprehensive testing process before full deployment
One of the biggest mistakes in LLM implementation is assuming that if a model generates fluent responses, it must also be accurate. It doesn’t work that way. Language models optimize for probability, not truth. That means some answers will sound plausible but still be factually incorrect or misleading. Before you deploy an LLM in any production environment, that gap needs to be tested, thoroughly.
You need to assess the model’s behavior against real-world use cases. Start with the questions your users already ask. Look at historical data from customer service, internal ticketing systems, or existing chatbot workflows. See how the LLM responds to those questions, and how consistent the answers are across variations.
This test set should include both common queries and those that fall outside default training sets. The objective is to stress-test the system, instead of simply validating that it functions under ideal conditions. Focus your assessment on accuracy, clarity, tone, and fallback behavior when the model can’t provide a confident answer.
Deployments without structured testing are high-risk. You won’t know what’s broken until users point it out. By then, damage to efficiency, trust, or compliance could already be done. Decision-makers should treat this testing phase as part of core system development, not as a post-launch cleanup.
Once the system is live, testing doesn’t stop. Build in processes for continuous feedback and model iteration. Every edge case you collect improves system capability and resilience. What you’re aiming for is consistent, high-reliability performance that meets your standards and scales with confidence.
Key takeaways for decision-makers
- Evaluate performance gap: Only deploy an LLM if it clearly performs better than your current human or chatbot solution. Benchmark against real user interactions to ensure the system delivers accurate, efficient, and context-aware responses.
- Mitigate legal exposure: Leaders should assess legal risk before deployment, especially in regulated industries. Establish safeguards to prevent LLMs from generating misleading or non-compliant content at scale.
- Analyze true cost: Don’t assume LLMs are cheaper by default. Factor in customization, infrastructure, ongoing maintenance, and potential future increases in platform pricing.
- Commit to structured maintenance: Treat LLM upkeep as a long-term investment. Without a defined process for updates and error correction, system reliability will decline over time.
- Prioritize real-world testing: Build a rigorous testing pipeline using real user questions to validate LLM behavior before launch. Leaders should use this data to refine output quality and avoid avoidable errors in production.
