AI Accuracy Explained and How to Improve It

Your evals pass. Your customers still get the wrong answer. When a chatbot misstates a policy or an AI-drafted filing goes out unverified, the cost lands as bad decisions, lost trust, and legal exposure, not as an engineering ticket.

Defining AI accuracy

Classifier accuracy is correct predictions divided by total predictions, expressed as a percentage. A model that correctly classifies 90 out of 100 spam emails has 90% accuracy. A confusion matrix sorts predictions into true positives (TP), true negatives (TN), false positives (FP), or false negatives (FN).

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Why accuracy alone misleads on imbalanced data

In a heavily imbalanced dataset, one class may appear only 1% of the time, so a model that always predicts the majority class can look highly accurate while failing the task that matters. In fraud detection, a model that labels every transaction legitimate scores well and catches zero fraud.

Precision, recall, and F1-score

Three companion metrics expose what accuracy hides:

Assessing AI accuracy with current benchmarks

Your benchmark score can vary widely by task and evaluation design:

• Stanford's AI Index 2026 found hallucination rates across 26 top models of 22%–94% on open-ended factual questions.
• The same report counted documented AI incidents rising to 362 in 2025, up from 233 in 2024.
• On document summarization, the same report found hallucination rates of 1.8%–5.4%, with most models clustering at 4%–5%.

These findings measure different tasks and failure modes, and the AI Index also observes that "evaluations intended to be challenging for years are saturated in months." McKinsey's State of AI survey found 47% of respondents reported at least one negative consequence from AI use, and a separate AI trust survey found 74% identify inaccuracy as a highly relevant AI risk.

Understanding why AI accuracy is non-negotiable

When you ship AI into high-stakes workflows, accuracy failures become operational, regulatory, and customer risks.

Healthcare: A cross-sectional study in JAMA Health Forum examined 691 Food and Drug Administration (FDA)-cleared AI/machine learning (ML) medical devices. It found 489 adverse events involving 36 devices, including 458 malfunctions, 30 injuries, and one death.

Financial services: The European Union (EU) AI Act text classifies credit scoring systems as high-risk. Article 15 requires these systems to "achieve an appropriate level of accuracy, robustness, and cybersecurity" throughout their lifecycle, so validation is part of operating the system, not a one-time approval.

Customer-facing natural language processing (NLP): When your assistant gives wrong instructions on tax, benefits, or support questions, you own the deployed system's behavior.

Identifying what undermines AI accuracy in production

Data quality, drift, and hallucinations can erode production accuracy. Diagnose the source before swapping models, because each failure needs a different intervention.

Data quality

Models learn data's omissions, inconsistencies, and errors. Poor labels distort performance, while duplicate records inflate it when similar examples enter both training and eval sets. Review label definitions, class balance, missing fields, duplicates, and leakage before tuning.

Drift between training and production

Production data shifts: customer behavior changes, product surfaces evolve, policies update, retrieval corpora grow stale, and prompts drift. Aggregate scores can hide declines within one customer segment.

A SaaS bot may pass an old eval after a billing policy change. The model still answers confidently, and customers hit the failure first unless you compare production traces with current policy.

Hallucinations in generative AI

Generative AI hallucinations are confident false outputs, often grammatically clean enough that style or format checks miss them. Research from OpenAI and Georgia Tech offers a structural explanation: "Language models hallucinate because training and evaluation procedures reward guessing over acknowledging uncertainty," according to hallucination research.

Hallucinations begin in different places: the model may lack the knowledge, retrieved evidence may be irrelevant, or the prompt may pressure it to answer without enough context. Check both the answer and the evidence behind it and give the system an explicit way to abstain when evidence is thin.

Measuring AI accuracy

When you're evaluating generative AI, many correct answers won't match a single reference string.

N-gram metrics are no longer enough

Bilingual evaluation understudy (BLEU) measures n-gram precision against reference text; recall-oriented understudy for gisting evaluation (ROUGE) emphasizes recall, which suits summarization.

Both BLEU and ROUGE help when a reference exists, but overlap is a weak proxy when a correct answer uses different wording. Bidirectional encoder representations from transformers score (BERTScore) compares contextual embeddings instead of exact words, so it tolerates paraphrase.

Calibrating a large language model (LLM) judge

For open-ended outputs, a language model can grade another model's responses. That helps when deterministic code or reference matching falls short, but judges can favor verbosity, familiar phrasing, or answers that resemble their own style, and they may apply rubrics inconsistently when criteria overlap.

A verbose benefits answer can look helpful to a judge while an expert flags it for omitting an eligibility exception. Calibrate judges against human expert labels using representative samples, clear rubrics, and disagreement analysis, and recheck after prompt, model, or domain changes.

Groundedness metrics for RAG systems

Retrieval-augmented generation (RAG) applications need metrics that check whether outputs stay faithful to retrieved context, including faithfulness, answer relevance, context relevance, and grounding.

Context Adherence checks for closed-domain hallucinations — cases where the model states information the supplied context doesn't support. Correctness addresses open-domain factual errors. Irrelevant context points to retrieval fixes such as chunking, ranking, or query construction; sufficient context points to generation. Score retrieval and generation separately.

Measuring production agent accuracy

Production agents take multi-step actions, behave stochastically across runs, and propagate errors downstream. A correct final answer can hide a wrong tool call, an ignored result, or a loop the agent escaped late.

Agent evals therefore score complete trajectories. Your checks should ask:

• Did the agent complete every goal in the session?
• Did it select the correct tools with the correct arguments?
• Did it use tool outputs correctly in later reasoning?

Useful agentic metrics include Action Completion, which asks whether the agent accomplished all of your goals, and Tool Selection Quality, which evaluates whether it selected the correct tool and arguments. Score those separately, because an agent can pick the right tool on every step and still end the session without finishing the task.

How to improve AI accuracy

When you're improving an LLM system, a practical accuracy sequence is prompt engineering first, then RAG, then fine-tuning, with continuous evals running throughout.

1. Start with prompt engineering. OpenAI's guidance states that "prompt engineering is typically the best place to start," and is "often the only method needed for use cases like summarization, translation, and code generation".
2. Add RAG for knowledge problems. RAG is the right lever when the model needs current policies, proprietary documents, or customer-specific records at inference time.
3. Fine-tune for persistent behavior change. Supervised fine-tuning suits durable style, format, or domain behavior changes, though OpenAI is winding down its fine-tuning platform for teams newly adopting it.
4. Evaluate continuously. Run evals on every meaningful change, use agent observability to identify new failure modes, and grow the eval set as your customers expose edge cases.

Building AI accuracy into production workflows

Production accuracy depends on data quality checks, drift detection, and observability.

Splunk is extending end-to-end observability into the agentic era as the intelligence layer for trusted agentic operations across the enterprise. Splunk Agent Observability turns evals into operational control while advancing Splunk's mission of keeping digital systems secure and reliable:

• Agent Visibility views: See multi-agent decision paths, tool calls, handoffs, and workflow branches while debugging.
• Signals: Surface recurring and previously unknown failure patterns across production traces.
• Luna evaluation models: Run production-scale evaluations at up to 96% lower cost than frontier LLM-as-a-judge evaluations.
• Evaluation metrics: Score Action Completion, Tool Selection Quality, Reasoning Coherence, and other agentic behaviors.

Read The Agentic Shift to see how agent observability and evaluation improve AI accuracy in production.

Answering AI accuracy FAQs

Your evals pass. Your customers still get the wrong answer. When a chatbot misstates a policy or an AI-drafted filing goes out unverified, the cost lands as bad decisions, lost trust, and legal exposure, not as an engineering ticket.

Defining AI accuracy

Classifier accuracy is correct predictions divided by total predictions, expressed as a percentage. A model that correctly classifies 90 out of 100 spam emails has 90% accuracy. A confusion matrix sorts predictions into true positives (TP), true negatives (TN), false positives (FP), or false negatives (FN).

Accuracy = (TP + TN) / (TP + TN + FP + FN)

Why accuracy alone misleads on imbalanced data

In a heavily imbalanced dataset, one class may appear only 1% of the time, so a model that always predicts the majority class can look highly accurate while failing the task that matters. In fraud detection, a model that labels every transaction legitimate scores well and catches zero fraud.

Precision, recall, and F1-score

Three companion metrics expose what accuracy hides:

Assessing AI accuracy with current benchmarks

Your benchmark score can vary widely by task and evaluation design:

• Stanford's AI Index 2026 found hallucination rates across 26 top models of 22%–94% on open-ended factual questions.
• The same report counted documented AI incidents rising to 362 in 2025, up from 233 in 2024.
• On document summarization, the same report found hallucination rates of 1.8%–5.4%, with most models clustering at 4%–5%.

These findings measure different tasks and failure modes, and the AI Index also observes that "evaluations intended to be challenging for years are saturated in months." McKinsey's State of AI survey found 47% of respondents reported at least one negative consequence from AI use, and a separate AI trust survey found 74% identify inaccuracy as a highly relevant AI risk.

Understanding why AI accuracy is non-negotiable

When you ship AI into high-stakes workflows, accuracy failures become operational, regulatory, and customer risks.

Healthcare: A cross-sectional study in JAMA Health Forum examined 691 Food and Drug Administration (FDA)-cleared AI/machine learning (ML) medical devices. It found 489 adverse events involving 36 devices, including 458 malfunctions, 30 injuries, and one death.

Financial services: The European Union (EU) AI Act text classifies credit scoring systems as high-risk. Article 15 requires these systems to "achieve an appropriate level of accuracy, robustness, and cybersecurity" throughout their lifecycle, so validation is part of operating the system, not a one-time approval.

Customer-facing natural language processing (NLP): When your assistant gives wrong instructions on tax, benefits, or support questions, you own the deployed system's behavior.

Identifying what undermines AI accuracy in production

Data quality, drift, and hallucinations can erode production accuracy. Diagnose the source before swapping models, because each failure needs a different intervention.

Data quality

Models learn data's omissions, inconsistencies, and errors. Poor labels distort performance, while duplicate records inflate it when similar examples enter both training and eval sets. Review label definitions, class balance, missing fields, duplicates, and leakage before tuning.

Drift between training and production

Production data shifts: customer behavior changes, product surfaces evolve, policies update, retrieval corpora grow stale, and prompts drift. Aggregate scores can hide declines within one customer segment.

A SaaS bot may pass an old eval after a billing policy change. The model still answers confidently, and customers hit the failure first unless you compare production traces with current policy.

Hallucinations in generative AI

Generative AI hallucinations are confident false outputs, often grammatically clean enough that style or format checks miss them. Research from OpenAI and Georgia Tech offers a structural explanation: "Language models hallucinate because training and evaluation procedures reward guessing over acknowledging uncertainty," according to hallucination research.

Hallucinations begin in different places: the model may lack the knowledge, retrieved evidence may be irrelevant, or the prompt may pressure it to answer without enough context. Check both the answer and the evidence behind it and give the system an explicit way to abstain when evidence is thin.

Measuring generative AI accuracy

When you're evaluating generative AI, many correct answers won't match a single reference string.

N-gram metrics are no longer enough

Bilingual evaluation understudy (BLEU) measures n-gram precision against reference text; recall-oriented understudy for gisting evaluation (ROUGE) emphasizes recall, which suits summarization.

Both BLEU and ROUGE help when a reference exists, but overlap is a weak proxy when a correct answer uses different wording. Bidirectional encoder representations from transformers score (BERTScore) compares contextual embeddings instead of exact words, so it tolerates paraphrase.

Calibrating a large language model (LLM) judge

For open-ended outputs, a language model can grade another model's responses. That helps when deterministic code or reference matching falls short, but judges can favor verbosity, familiar phrasing, or answers that resemble their own style, and they may apply rubrics inconsistently when criteria overlap.

A verbose benefits answer can look helpful to a judge while an expert flags it for omitting an eligibility exception. Calibrate judges against human expert labels using representative samples, clear rubrics, and disagreement analysis, and recheck after prompt, model, or domain changes.

Groundedness metrics for RAG systems

Retrieval-augmented generation (RAG) applications need metrics that check whether outputs stay faithful to retrieved context, including faithfulness, answer relevance, context relevance, and grounding.

Context Adherence checks for closed-domain hallucinations — cases where the model states information the supplied context doesn't support. Correctness addresses open-domain factual errors.Irrelevant context points to retrieval fixes such as chunking, ranking, or query construction; sufficient context points to generation. Score retrieval and generation separately.

Measuring production agent accuracy

Production agents take multi-step actions, behave stochastically across runs, and propagate errors downstream. A correct final answer can hide a wrong tool call, an ignored result, or a loop the agent escaped late.

Agent evals therefore score complete trajectories. Your checks should ask:

• Did the agent complete every goal in the session?
• Did it select the correct tools with the correct arguments?
• Did it use tool outputs correctly in later reasoning?

Useful agentic metrics include Action Completion, which asks whether the agent accomplished all of your goals, and Tool Selection Quality, which evaluates whether it selected the correct tool and arguments. Score those separately, because an agent can pick the right tool on every step and still end the session without finishing the task.

Agent Leaderboard v2 shows why both matter. Gemini-2.5-flash scored 94% on Tool Selection Quality but only 38% on Action Completion. GPT-4.1 led at 62% Action Completion across five enterprise domains.

How to improve AI accuracy

When you're improving an LLM system, a practical accuracy sequence is prompt engineering first, then RAG, then fine-tuning, with continuous evals running throughout.

1. Start with prompt engineering. OpenAI's guidance states that "prompt engineering is typically the best place to start," and is "often the only method needed for use cases like summarization, translation, and code generation".
2. Add RAG for knowledge problems. RAG is the right lever when the model needs current policies, proprietary documents, or customer-specific records at inference time.
3. Fine-tune for persistent behavior change. Supervised fine-tuning suits durable style, format, or domain behavior changes, though OpenAI is winding down its fine-tuning platform for teams newly adopting it.
4. Evaluate continuously. Run evals on every meaningful change, use agent observability to identify new failure modes, and grow the eval set as your customers expose edge cases.

Building AI accuracy into production workflows

Production accuracy depends on data quality checks, drift detection, and observability.

Splunk is extending end-to-end observability into the agentic era as the intelligence layer for trusted agentic operations across the enterprise. Splunk Agent Observability turns evals into operational control while advancing Splunk's mission of keeping digital systems secure and reliable:

• Agent Visibility views: See multi-agent decision paths, tool calls, handoffs, and workflow branches while debugging.
• Signals: Surface recurring and previously unknown failure patterns across production traces.
• Luna evaluation models: Run production-scale evaluations at up to 96% lower cost than frontier LLM-as-a-judge evaluations.
• Evaluation metrics: Score Action Completion, Tool Selection Quality, Reasoning Coherence, and other agentic behaviors.

Read The Agentic Shift to see how agent observability and evaluation improve AI accuracy in production.

FAQs about AI accuracy