Home Blog LLM 6 Questions About AI Takeoffs for Casework Shops You’re about to spend significant money and management attention on an AI system. Whether it succeeds or becomes expensive technical debt depends almost entirely on what you nail down in the first few meetings. Here are six questions that expose which vendors have built systems designed to work over time and which ones are hoping you won’t look too closely. The questions below are designed to guide those early meetings. They are not traps to catch anyone out. Instead, they are benchmarks you want both sides to agree on before any work begins. 1. What accuracy will you guarantee? This question filters out half the vendors in five minutes. An honest answer requires specifics (like 85% or 90%) measured against a defined test set. Anything vague is a red flag. Watch out for phrases like “very accurate,” “industry-leading,” or even “around 90%.” None of those mean anything without a clear measurement method. “Around 90%” is particularly risky because without a baseline, it is just a number generated for the meeting. An experienced vendor will give you a range, anchor it to a specific measurement method, and explain how that number is validated. Look for commitments like this: “We aim for 85–90% accuracy on your specific data, measured against your test set, with the live dashboard ready by week eight.” That is a team you can sign a contract with. 2. How will we measure accuracy? Committing to a number is only the first step. You also need to know how that number tracks once the project goes live. If a vendor says, “you’ll see it works,” keep looking. That is a feeling, not a metric. When something goes wrong six months down the line, you will have no data to pinpoint where the system is failing. A reliable measurement framework relies on three separate elements: A “ground truth” baseline: A file of real examples paired with their correct results to test the AI against regularly. Error categorization: A system that sorts mistakes by type (such as missed data, misidentifications, or mixed-up categories) rather than just labeling them “wrong.” Historical tracking: A continuous record to show whether accuracy is steady, climbing, or slipping over time. Without these three pieces, you are managing by guesswork. 3. What’s included in your evaluation pipeline? This is the question most vendors hope you skip. The evaluation pipeline is simply the system built to check, track, and improve the AI after launch. Without it, a model might perform perfectly on day one but quietly degrade a few months later without anyone noticing. If a vendor treats this setup as optional or an expensive upsell, they likely plan to launch and walk away. That works for a demo, but not for a system your business will rely on for years. A robust pipeline always covers four core areas: the ground truth file, a live health dashboard, a feedback loop for users to flag mistakes, and safety checks to ensure new updates do not break features that already work. If a vendor cannot describe these four pieces concretely, they probably have not built them. 4. How do we improve accuracy after launch? You are not just buying an AI model as it performs on day one. You are investing in a system that needs to adapt over the next two years. Whether it improves or quietly degrades depends on the post-launch plan. Be cautious if the answer sounds like: “Submit a support ticket and we’ll take a look.” That is just a service contract. Every single tweak will turn into a billable scope discussion. Eventually, you will stop asking, the system will plateau, and you will have to settle for mediocre results. Instead, look for a built-in feedback cycle. Users flag errors directly in the software. Those flagged cases automatically feed back into the training data. The model retrains, and automated tests verify the change fixed the issue without causing new bugs. That is a system designed to scale efficiently. 5. Can we see accuracy metrics in real time? You need direct access to these metrics. This isn’t about checking the numbers every morning; it is about transparency. The moment you lose independent visibility, you only know what the vendor chooses to tell you. AI accuracy fluctuates. New clients might upload documents in an unexpected format, or new product lines might shift the data baseline. If you only discover a drop in accuracy during a quarterly review, that issue has already cost you money for three months. Insist on a shared dashboard from the start. Ask to see a sample version before signing. If it does not exist yet, secure a clear timeline for when it will be delivered and exactly what metrics it will track. 6. What happens when accuracy drops? Here is the reality: accuracy always drops eventually. A client will use non-standard templates, an edge case will slip through, or real-world conditions will shift. The goal is to ensure the vendor has anticipated this breakdown. “That shouldn’t happen” is a hope, not a strategy. A mature response balances three operational steps: automated alerts that trigger the moment accuracy dips below your agreed threshold, a clear diagnostic process to find the root cause, and a pre-budgeted retraining workflow. With these three safeguards, a drop in accuracy is a minor adjustment. Without them, it is a business crisis. The pattern behind the questions All six questions point to a single core requirement: measurable accountability. A vendor who provides clear, structured answers across these areas might not be the cheapest or the fastest option, but they are the partner whose system will still be driving value a year after launch. Related articles //May 31, 2025 //LLM Chatting with Large PDFs (100–500 Pages): Using RAG with OpenAI Embeddings (Local vs. API) //December 26, 2024 //LLM Experiments with different LMMs //May 23, 2025 //Guides How to set up monitoring of CPU and memory usage for C++ multithreaded application with Prometheus, Grafana, and Process Exporter Let's collaborate Tell us a bit about your project or challenge, and we'll get back to you shortly. Volodymyr Hresko Co-Founder & COO Reach out directly [email protected] InstagramThis field is for validation purposes and should be left unchanged.Full name First Email DetailsBy submitting the form, you agree to Coxit’s Privacy Policy.
Home Blog LLM 6 Questions About AI Takeoffs for Casework Shops You’re about to spend significant money and management attention on an AI system. Whether it succeeds or becomes expensive technical debt depends almost entirely on what you nail down in the first few meetings. Here are six questions that expose which vendors have built systems designed to work over time and which ones are hoping you won’t look too closely. The questions below are designed to guide those early meetings. They are not traps to catch anyone out. Instead, they are benchmarks you want both sides to agree on before any work begins. 1. What accuracy will you guarantee? This question filters out half the vendors in five minutes. An honest answer requires specifics (like 85% or 90%) measured against a defined test set. Anything vague is a red flag. Watch out for phrases like “very accurate,” “industry-leading,” or even “around 90%.” None of those mean anything without a clear measurement method. “Around 90%” is particularly risky because without a baseline, it is just a number generated for the meeting. An experienced vendor will give you a range, anchor it to a specific measurement method, and explain how that number is validated. Look for commitments like this: “We aim for 85–90% accuracy on your specific data, measured against your test set, with the live dashboard ready by week eight.” That is a team you can sign a contract with. 2. How will we measure accuracy? Committing to a number is only the first step. You also need to know how that number tracks once the project goes live. If a vendor says, “you’ll see it works,” keep looking. That is a feeling, not a metric. When something goes wrong six months down the line, you will have no data to pinpoint where the system is failing. A reliable measurement framework relies on three separate elements: A “ground truth” baseline: A file of real examples paired with their correct results to test the AI against regularly. Error categorization: A system that sorts mistakes by type (such as missed data, misidentifications, or mixed-up categories) rather than just labeling them “wrong.” Historical tracking: A continuous record to show whether accuracy is steady, climbing, or slipping over time. Without these three pieces, you are managing by guesswork. 3. What’s included in your evaluation pipeline? This is the question most vendors hope you skip. The evaluation pipeline is simply the system built to check, track, and improve the AI after launch. Without it, a model might perform perfectly on day one but quietly degrade a few months later without anyone noticing. If a vendor treats this setup as optional or an expensive upsell, they likely plan to launch and walk away. That works for a demo, but not for a system your business will rely on for years. A robust pipeline always covers four core areas: the ground truth file, a live health dashboard, a feedback loop for users to flag mistakes, and safety checks to ensure new updates do not break features that already work. If a vendor cannot describe these four pieces concretely, they probably have not built them. 4. How do we improve accuracy after launch? You are not just buying an AI model as it performs on day one. You are investing in a system that needs to adapt over the next two years. Whether it improves or quietly degrades depends on the post-launch plan. Be cautious if the answer sounds like: “Submit a support ticket and we’ll take a look.” That is just a service contract. Every single tweak will turn into a billable scope discussion. Eventually, you will stop asking, the system will plateau, and you will have to settle for mediocre results. Instead, look for a built-in feedback cycle. Users flag errors directly in the software. Those flagged cases automatically feed back into the training data. The model retrains, and automated tests verify the change fixed the issue without causing new bugs. That is a system designed to scale efficiently. 5. Can we see accuracy metrics in real time? You need direct access to these metrics. This isn’t about checking the numbers every morning; it is about transparency. The moment you lose independent visibility, you only know what the vendor chooses to tell you. AI accuracy fluctuates. New clients might upload documents in an unexpected format, or new product lines might shift the data baseline. If you only discover a drop in accuracy during a quarterly review, that issue has already cost you money for three months. Insist on a shared dashboard from the start. Ask to see a sample version before signing. If it does not exist yet, secure a clear timeline for when it will be delivered and exactly what metrics it will track. 6. What happens when accuracy drops? Here is the reality: accuracy always drops eventually. A client will use non-standard templates, an edge case will slip through, or real-world conditions will shift. The goal is to ensure the vendor has anticipated this breakdown. “That shouldn’t happen” is a hope, not a strategy. A mature response balances three operational steps: automated alerts that trigger the moment accuracy dips below your agreed threshold, a clear diagnostic process to find the root cause, and a pre-budgeted retraining workflow. With these three safeguards, a drop in accuracy is a minor adjustment. Without them, it is a business crisis. The pattern behind the questions All six questions point to a single core requirement: measurable accountability. A vendor who provides clear, structured answers across these areas might not be the cheapest or the fastest option, but they are the partner whose system will still be driving value a year after launch. Related articles //May 31, 2025 //LLM Chatting with Large PDFs (100–500 Pages): Using RAG with OpenAI Embeddings (Local vs. API) //December 26, 2024 //LLM Experiments with different LMMs //May 23, 2025 //Guides How to set up monitoring of CPU and memory usage for C++ multithreaded application with Prometheus, Grafana, and Process Exporter
