Gains:
- Ability to distinguish where AI saves real time in the mining engineering workflow and where safety-critical responsibility should remain with the competent engineer, depending on the level of risk
- Ability to implement a multi-layered validation discipline that tests each AI output against standard, independent calculation and field measurement
- Ability to anonymize context and acquire the habit of choosing safe vehicles to protect reserve data, license information and personnel/production data
A mining engineer's job is to bridge the uncertainty below ground with the decisions above ground. Estimating the ore body from drilling data, calculating the reserve, planning the pit, managing the equipment fleet, monitoring slope behavior, ensuring occupational safety, measuring the environmental impact and reporting all of these in accordance with the legislation. The common feature of these jobs is that they work with a large number of measurements and data, but each decision results in human lives, millions of liras of investment and irreversible environmental consequences in the field. Artificial intelligence (AI for short; software systems that work on text, numbers and images with big data and language models) is a powerful aid in this data-intensive but decision-critical ground. This unit teaches you where to safely use AI in mining, where it is dangerous, and why you have to validate every AI output against standards, field measurement, and engineering judgment.
The central principle you will use throughout this module is: AI is an assistant, not a decision maker. A grade (metal ratio in the ore) value, a slope angle, a reserve figure, a safety limit or a legislative article does not become true just because "AI said so"; It is merely a hypothesis until verified by measurement, reference, and competent engineer judgment. You will see this sentence again in every unit of the module, because in mining, a wrong number often means the collapse of an investment, the collapse of a slope, or the injury of a person.
What is LLM and what does it do in mining?
LLM (Large Language Model) is a software that learns patterns from very large chunks of text and predicts the "next word" based on probability. This is the engine of tools such as ChatGPT, Claude, Gemini. The point is this: LLM is a "language estimator", not a "knowledge base". Speaks mining terminology fluently, but does not measure a grade; produces the number that looks most likely. Therefore, he or she may give confident but incorrect answers about average grade, stripping rate, a regulatory clause, or an explosive limit. This is called a hallucination (fabrication). Hallucination is not a malfunction, but an inherent feature of this technology; Therefore, verification is not an "additional step" but an integral part of the job.
Tasks where AI is strong in mining:
- Concept explanation and teaching: what is a variogram, how does the leverage rule work, how acid mine drainage occurs.
- Data cleaning and organization: turning scattered borehole logs into a coherent table, capturing unit mismatch.
- Generating a list of hypotheses: systematically listing possible causes of a decline in production.
- Coding: Data cleaning, graphing, geostatistics and anomaly analysis scripts with Python.
- Draft text: JSA (job safety analysis), environmental monitoring report, draft tender/technical specification.
- Brainstorm: scenario comparison, experiment/measurement plan ideas.
Where AI is weak and risky:
- Giving exact numerical value (grade, reserve, slope angle, limit value).
- Current and specific legislation/standard article and version information.
- Diagnosis without actual measurement data of your site.
- Safety-critical decision: blast design approval, evacuation decision, serviceability.
Risk-based classification: the filter before using AI
Not every task is at the same level of risk. Classify the task by risk level before using AI. The table below provides a decision framework that you will use throughout this module.
Risk level
sample task
AI role
Mandatory verification
low
Concept explanation, draft text, code skeleton
free use
Review is enough
medium
Data cleaning, hypothesis list, calculation setup
Draft/co-author
Manual control + source confirmation
high
Reserve estimation, pit limit, blasting parameter
idea generator
Standard + field measurement + software
critical
Evacuation decision, slope safety, serviceability/suitability decision
Draft/scan only
Competent engineer approval + accredited testing
Tip: If you've marked a task as "critical", the AI's output is never the final document. At most it can be a preliminary draft or checklist; The competent engineer undertakes the signature and responsibility.
End-to-end flow: embedding AI into your workflow
A typical problem-solving cycle in mining goes like this, with AI engaging in each step but not closing any of them alone:
- Define the problem: What to predict, what decision to make, what constraint is there? (AI: clarifies the question.)
- Collect and clean data: Drilling, production, monitoring, laboratory data. (AI: strong in cleanup and inconsistency checking.)
- Generate hypotheses/scenarios: Possible causes, alternative plans. (AI: produces systematic list.)
- Analyze: Calculation, geostatistics, model, image processing. (AI: generates code and calculation fiction.)
- Verify: Standard, field, standalone account, software. (AI is on the sidelines; humans and measurement decide.)
- Report and decide: The competent engineer takes responsibility. (AI: draft writer.)
Keep these six steps in mind; the rest of the module shows you how to use AI with concrete mining tasks, step by step.
three mini cases
Case 1 — Made-up average grade. An intern asks the AI chat tool about the average grade of a copper project; AI says “about 0.65% Cu.” The intern puts this in the investment presentation. However, AI has not seen a single drilling data of this project; produced a typical number for the "copper project". The real value comes only from the project's own composited drilling data and geostatistical estimate; The real value in this project is 0.38%. The difference is the difference between the investment appearing profitable and making a loss. The correct attitude is to never "ask" the grade for the AI, but calculate it from your own database.
Case 2 — Non-existent regulation clause. An engineer asks the AI the maximum ground vibration (PPV: peak particle velocity, mm/s) allowed in blasting. The AI says "10 mm/s according to regulation" and makes up an item number. The engineer writes the report accordingly. During the inspection, it is revealed that the item number is not real and the limit value varies depending on distance and building type. The correct attitude is to open the current regulation from the official source and confirm the value and substance. Even if the AI knows the name of the regulation correctly, it may hallucinate the article number.
Case 3 — Correct use. A geologist suspects a discrepancy in the grade column in a data set of 40 boreholes. It tells the AI to "list the rows in this table that may have unit inconsistencies, negative values, visible outliers, and empty cells, and write what you suspect for each; DO NOT correct, just mark." The AI flags a few lines mixed with % mixed with ppm, two negative grades, and a 999 code value. The geologist checks and corrects these one by one from the raw logs. Here AI was used correctly: attracted attention, human made the decision and correction.
Copiable prompt templates
You can use the templates below by adapting them to your own business. In each of them, there is a conscious instruction to "give an exact number, indicate the source, explain the uncertainty".
ROLE AND BOUNDARY TEMPLATE "Role: You are an experienced mining engineer assistant. Task: [write the subject]. Rules: When giving the exact numerical value (grade, reserve, limit), specify the SOURCE; if you are not sure, say 'must be verified'. If you give a regulation/standard article, give it with its version, but remind that it must be confirmed from the official source. At the end of the answer, write 'what needs to be verified by field measurement/calculation' item by item."
HYPOTHESIS GENERATION TEMPLATE"Produce a systematic list of possible reasons for the following situation: [describe the situation; what was observed, what data is available]. For each hypothesis: (1) what evidence supports it, (2) what measurement or calculation should be made to confirm/disprove it. MAKE a definitive diagnosis; produce an investigation roadmap."
VERIFICATION CHECKLIST TEMPLATE"List EVERY numeric value and EVERY regulation/standard reference in the report draft below. For each, indicate from which source (self-drilling data, regulatory text, laboratory report, field survey) it should be verified. Collect those of uncertain origin under 'cannot be used without verification'. Text: [paste]."
ANONYMIZATION PRE-CHECK TEMPLATE "Before giving the following text to an AI tool, flag trade secret or personal/regulatory sensitive parts: reserve numbers, license number, actual coordinates, customer/staff names, confidential cost data. Suggest how I can anonymize these. Text: [paste]."
Weak prompt / Strong prompt
Asking the same question two ways will yield very different results.
WEAK PROMPT: "How much is the reserve of this mine?"
STRONG PROMPT: "Role: You are the assistant resource geologist. Explain which steps a reserve/resource estimate needs to go through (compositing, variogram, kriging, classification, economic limit) and which data and which competent person's approval is required at each step. DO NOT GIVE AN EXACT reserve number; emphasize that this can only be calculated from the project's own data and should be reported within the framework of a code such as JORC/UMREK."
Weak prompt invites AI to make up; The strong prompt keeps it in the right job (process description) and prevents the digital hallucination from happening in the first place.
Common mistakes
- “Asking” the AI for course-specific numbers such as grade, reserve, etc. AI does not know your data; these numbers are calculated solely from your own database.
- Using the legislation/standard clause without confirming it. The item number and value are frequently hallucinated.
- Basing safety-critical decision on AI output. Evacuation, blasting approval and serviceability decisions are made by the competent engineer.
- Pasting sensitive data into the cloud tool without anonymizing it. Reserve, coordinate and cost data are trade secrets.
- Waiting for one question and one answer. Use AI iteratively: generate drafts, critique, verify, correct.
Caution: The more precise and confident a number appears in an AI output, the higher the need for verification. Precision is not a guarantee of accuracy.
In summary
In this unit you have seen that AI is an assistant in mining, not a decision maker. LLMs are language predictors; It is powerful in concept explanation, data cleaning, hypothesis and code generation, but risky in producing precise numbers and legislation (hallucination). Classify tasks as low/medium/high/critical and adjust verification depth accordingly. Verify every numerical value from your own data or field measurement, every regulatory reference from the official source. Security-critical decisions always remain with the competent engineer and anonymize sensitive data before use.
Application task
Choose five tasks from your own business (or a fictitious mining project): a concept description, a data cleaning, a list of hypotheses, a reserve/numerical value, and a regulatory question. Place each in the risk table above. Then outsource two low/medium risk tasks to the AI with the “Role and boundary” template and mark each numerical/regulatory element of the output with the “Verification checklist” template. Write down which elements cannot be used without verification.
checklist
- [ ] I placed the mission at risk level (low/medium/high/critical) before using it.
- [ ] I calculated field-specific numbers such as grade, reserve, etc. from my own yield instead of asking the AI.
- [ ] I have verified each legislation/standard reference from the official source.
- [ ] I anonymized sensitive data (reserve, coordinates, name, cost).
- [ ] I left the safety-critical decision to competent engineer approval, not based on AI output.
- [ ] I scanned every numerical value in the AI output for "cannot be used without verification".