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Choose an AI model by starting with the business task, not a leaderboard. Describe the workload, define measurable success and unacceptable failures, test a small shortlist on representative examples, and then verify latency, throughput, cost, data controls, integration and governance in a production-like setup. The model that wins the benchmark is not necessarily the model that best fits your process.
This method works for chat, extraction, classification, summarization, retrieval-augmented generation (RAG), embeddings, image and audio workloads, multimodal applications and tool-using agents. It also gives you an auditable reason for the decision and a trigger for revisiting it when the model, workload or requirements change.
1. Describe the business workload before looking at models
A model can be excellent at one job and unsuitable for another. Start with a one-page workload description that someone outside the project could understand. Microsoft’s workload-selection framework treats task fit, modality, context, security, region, deployment strategy, domain specificity, performance and tunability as selection filters (Microsoft Learn guidance).
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Record the task and output contract
- Task: State the action in a verb: classify a claim, extract fields from an invoice, summarize a support call, draft a reply, answer questions over policy documents, generate an image, transcribe audio or route a request to a tool.
- Input and output: Specify text, images, audio, video, documents, structured records or combinations. Define the required schema, length, language, citations, formatting and whether abstaining is allowed.
- Users and operating context: Identify who receives the output, what systems surround it, and whether a person reviews it before action.
- Workload shape: Capture requests per hour or day, burstiness, concurrency, peak periods, context size and retention requirements.
- Service target: State acceptable end-to-end response time, availability, retry behavior and any batch-versus-real-time distinction.
- Error consequence: Separate harmless style defects from errors that could cause financial loss, privacy exposure, unsafe advice, discrimination or regulatory harm.
Do not describe a vague goal such as “use AI to improve productivity.” Rewrite it as a testable operation, for example: “For incoming warranty emails, identify the product, purchase date and issue type, return valid JSON, and send uncertain cases to a human queue.”
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Match modality and capability
| Workload | Capabilities to verify | Typical failure to test |
|---|---|---|
| Classification or routing | Stable labels, calibrated confidence, structured output and low latency | Confusing similar classes or forcing a label when the correct action is “unknown” |
| Extraction | Document or vision input, schema-constrained output, OCR tolerance and field-level accuracy | Inventing a missing value or shifting a value to the wrong field |
| Summarization | Long-context handling, factuality, coverage and controllable length | Omitting a critical exception or adding details absent from the source |
| Question answering with private knowledge | RAG compatibility, citation or quotation support, access control and current-data handling | Answering from general knowledge when the approved source is silent |
| Embeddings and semantic search | Vector representation quality, multilingual behavior, indexing and retrieval latency | Returning text that is topically similar but operationally irrelevant |
| Image, audio or multimodal work | The required modality, resolution or duration limits, transcription or vision quality, and cross-modal reasoning | Missing visual details, mishearing names or failing when inputs combine media types |
| Tool-using agents | Planning, tool selection, argument validity, authorization, recovery and observability | Calling the wrong tool, repeating actions or taking an irreversible action without confirmation |
2. Turn requirements into measurable success and failure criteria
Write the scorecard before running a comparison. A useful scorecard distinguishes output quality from operational and risk requirements, so a cheap but unreliable model cannot appear to win by price alone.
Define what “correct” means
- Correctness: Is the answer or extracted value factually right?
- Completeness: Are all required fields, steps, exceptions or source points present?
- Grounding: For source-based answers, can each material claim be traced to approved content?
- Format validity: Does the response parse as the required JSON, XML, table or command?
- Readability and usefulness: Can the intended user act on it without rewriting?
- Safety and privacy: Does it refuse or escalate prohibited requests and avoid exposing sensitive data?
- Abstention: Does it decline, ask for clarification or route to a person when evidence is insufficient?
For consequential decisions, define unacceptable errors separately from ordinary defects. A missed low-value category may be tolerable; an incorrect eligibility decision may not be. Consider whether performance must be understood across affected groups and whether users need an explanation. The UK Information Commissioner’s Office advises documenting how application impact, data type, transparency requirements and interpretability influence model choice (ICO documentation guidance).
Choose metrics that reflect the workflow
Use task-appropriate measures rather than one universal score. Classification may need per-class precision, recall and abstention behavior. Extraction may need exact or normalized field matches. Summarization needs factual coverage and omission checks. Retrieval needs relevance of the returned passages, while a RAG answer also needs groundedness. A conversational system may require human ratings for usefulness, tone and policy adherence. Record group-specific error rates where the use case or law makes them relevant.
Set a decision rule in advance. For example, a candidate must meet the minimum safety and format requirements, then the team can trade quality against cost and latency. This prevents a high average score from hiding a catastrophic edge case.
3. Build a representative evaluation set
Your evaluation set is the evidence behind the decision. AWS recommends using historical workflow examples, expert-annotated cases and verified references, while preserving ordinary requests, edge cases and the actual input distribution (AWS proof-of-concept guidance).
Assemble and validate examples
- Sample real inputs: Pull de-identified cases from the workflow instead of writing only ideal prompts. Include short and long inputs, common languages, document variants and the normal rate of ambiguous requests.
- Add difficult cases: Include missing information, contradictory instructions, typos, unusual formatting, adversarial wording, sensitive content and cases that should be escalated.
- Create references: Have subject-matter experts produce the expected answer, fields, citations or action. Mark acceptable alternative answers where more than one response is valid.
- Check the references: Resolve disagreements and record the reason for each label. A flawed “gold” answer makes every model look wrong or right for the wrong reason.
- Separate development and holdout data: Use one portion to refine prompts and settings and keep a sealed portion for the final comparison. Do not repeatedly tune against the same holdout examples.
- Protect information: Remove unnecessary personal data, restrict access and confirm that sending examples to an external service is permitted.
Do not select examples because one candidate performs well on them. If the production distribution changes by season, product line or region, preserve those proportions or run separate slices so the result is not an average that no real user experiences.
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4. Create a practical shortlist
Compare a small, credible set rather than every available model. A useful shortlist normally includes a currently proven option, a capable general-purpose candidate and a lower-cost or specialized model that meets the modality and safety requirements. AWS specifically cautions that “the largest foundation model is not necessarily always the best model for your use case” (AWS model-selection guidance).
| Candidate role | Why include it | What it reveals |
|---|---|---|
| Proven incumbent | Provides a realistic quality and reliability baseline | Whether a change is worth migration effort and risk |
| High-capability general model | Tests the upper end of quality, context and reasoning for the task | Whether difficult cases justify additional cost or latency |
| Lower-cost or smaller model | Tests whether the workload can meet requirements economically | Potential savings, throughput gains and failure trade-offs |
| Specialized or open model | Tests domain, modality, deployment or customization advantages | Whether control or domain fit outweighs operational overhead |
This is a decision set, not a required number of models. Remove any candidate that fails a non-negotiable requirement such as the needed modality, context size, region, data-use terms or structured-output support before spending time on detailed scoring.
5. Run a fair, task-specific comparison
Send the same evaluation cases through each candidate with equivalent instructions, retrieval context, tool permissions, safety filters and output constraints. Record model name and version, prompt or template, parameters, retrieval configuration, timestamp and raw output. Without that record, a later result cannot be reproduced.
Score outputs and inspect failures
Calculate the agreed metrics, but do not choose on averages alone. Review false positives, false negatives, hallucinations, malformed responses, refusals, latency outliers and examples where reviewers disagree. Segment results by language, document type, customer group, complexity and confidence band when those slices matter.
| Comparison axis | Questions to answer | Evidence to retain |
|---|---|---|
| Task and modality fit | Does it accept every required input and produce the required output or tool call? | Capability documentation and pass/fail cases |
| Quality | How accurate, complete, grounded, readable and policy-compliant are results? | Metric results, reviewer notes and representative failures |
| Error profile | Which errors occur, how often, and what is their business impact? | Severity-weighted error log and escalation behavior |
| Performance | What are end-to-end latency, throughput, concurrency limits, context handling and error rates under realistic load? | Load-test traces and service-level observations |
| Economics | What is the cost per request and per completed workflow at expected volume? | Usage assumptions and total-cost calculation |
| Operations | Can the team monitor, update, secure and troubleshoot it? | Runbook, ownership and integration assessment |
| Governance | Can you satisfy privacy, security, regional, explainability and record-keeping obligations? | Data-flow review, contracts, controls and decision record |
6. Test production constraints and total cost
A model that looks strong in a notebook can fail when many users call it at once. Measure the complete path from request arrival to the user-visible result, including retrieval, safety checks, tool calls, retries, post-processing and human handoffs.
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Measure the system, not just the model endpoint
- Latency: Track typical and worst-case response times, including time to first token when streaming and time to a complete, validated answer.
- Throughput and concurrency: Test expected average and peak demand. Observe throttling, queue growth, rate limits and the effect of retries.
- Reliability: Record timeouts, transient errors, malformed outputs, tool failures and recovery success.
- Context behavior: Verify the maximum practical document or conversation size, truncation rules and retrieval quality as context grows.
- Cost: Include input and output usage, requests, embedding and retrieval services, reranking, storage, network egress, observability, human review and engineering operations. For self-hosting, include accelerator hours, storage, capacity planning, power and maintenance.
Compare cost per completed business outcome, not merely cost per token. A cheaper model that causes more retries or human corrections may have a higher workflow cost. Conversely, a larger model may be wasteful when a smaller candidate meets the quality and risk thresholds.
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Choose a deployment pattern
| Pattern | Advantages | Trade-offs to assess |
|---|---|---|
| Managed model API | Fastest start, provider-managed infrastructure and access to multiple models | Data-transfer terms, regional availability, rate limits, dependency on provider behavior and usage pricing |
| Managed AI platform | Shared evaluation, routing, monitoring, identity and governance features | Platform costs, configuration complexity and possible coupling to platform-specific interfaces |
| Self-hosted or open model | More control over data path, versioning, tuning and capacity | Hardware, scaling, patching, security, serving expertise, model license and ongoing maintenance |
Security review should cover encryption, identity and access, tenant isolation, logging, retention, training use, secrets handling and incident response. Confirm where data is processed and whether the required region is available before selecting a provider or deployment mode.
7. Decide whether you need prompting, RAG, fine-tuning or an agent
Model selection and application design are linked. Start with the least complex approach that can meet the requirements, then add components only when testing shows a specific gap.
| Approach | Use it when | Test these risks |
|---|---|---|
| Prompt or context engineering | The task relies on general knowledge, clear instructions, examples and stable output rules | Instruction following, ambiguity, prompt injection and behavior drift |
| RAG | Answers must use private, current or frequently changing material | Access control, retrieval recall, stale indexes, citation accuracy and unsupported answers |
| Fine-tuning | Repeated domain patterns, style or structured behavior cannot be achieved reliably with prompting and context | Training-data quality, overfitting, version management, privacy and regression on ordinary cases |
| Tool-using agent | The workflow requires planning across systems or actions rather than one response | Tool authorization, argument validation, loops, side effects, recovery, latency and auditability |
AWS’s proof-of-concept guidance and Microsoft’s selection guidance both treat this architecture choice as part of evaluation, not as an afterthought (AWS guidance; Microsoft guidance). For an agent, evaluate the complete workflow: reasoning, orchestration, external-tool reliability, authorization and security, not just the text produced at the end.
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Governance requirements can eliminate an otherwise attractive candidate. Map the data flow, lawful basis, retention, access controls, cross-border transfers, sensitive attributes and human oversight before a pilot uses real records.
UK documentation and impact considerations
The ICO’s documentation guidance is UK-focused. It calls for recording the domain’s transparency requirements, how application impact and data type affect model choice, why a less interpretable approach is used when applicable, what explanations and risk controls support it, selected metrics, group-specific error rates where relevant, testing, monitoring frequency and model-version changes (ICO documentation guidance). Treat these as documentation and risk-management expectations, not as a universal technical recipe.
European Union scope and dates
The European Commission states that provider obligations for general-purpose AI models under the EU AI Act entered into application on 2 August 2025, and its enforcement powers began on 2 August 2026 (Commission policy guidance). The Commission’s FAQ describes provider documentation, copyright-policy and training-content-summary duties, with additional evaluation and risk-mitigation obligations for models that pose systemic risks (Commission FAQ). These are provider obligations; whether they apply to your organization depends on its role, product, deployment and other facts. Obtain jurisdiction-specific legal advice for a regulated use.
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Banking model-risk guidance
The Federal Reserve’s interagency supervisory guidance is dated April 17, 2026. It covers model-risk management for banking organizations and states that generative and agentic AI models are outside its scope, while noting that governance practices should guide controls for tools and systems not covered (Federal Reserve guidance). Do not treat it as a direct generative-AI rule; use it as context for control design where applicable.
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A staged evaluation limits cost while exposing failures early. Keep the business owner, subject-matter reviewer, security or privacy representative and technical operator involved from the beginning.
- Write the workload brief: Record task, modalities, users, volume, latency, error costs, data classes, regions, integrations and non-negotiable requirements.
- Approve the evaluation set: Document sampling, labels, references, edge cases, holdout data and access controls.
- Screen capability: Eliminate candidates that cannot meet modality, context, output, region, security or policy requirements.
- Run a controlled quality test: Use identical cases and application settings, score against the rubric and review severe failures.
- Load-test the finalists: Measure end-to-end latency, throughput, concurrency, reliability and cost under expected and peak conditions.
- Test the application pattern: Compare prompt-only, RAG, fine-tuned or agent designs when more than one is plausible. Include retrieval and tool behavior in the score.
- Conduct governance review: Confirm data handling, access, retention, region, explanations, human oversight, logging and incident response.
- Make a documented decision: State the selected model and version, alternatives considered, evidence, trade-offs, exceptions, owners and approval.
- Pilot with safeguards: Use limited users, reversible actions, escalation paths and monitoring before expanding.
10. Worked example: selecting a model for invoice intake
Suppose an accounts-payable team wants to extract supplier, invoice number, date, currency, tax and total from emailed PDFs, then send uncertain records to a reviewer. The workload brief would specify PDF and image inputs, the exact JSON schema, supported languages, daily and peak volumes, acceptable processing time, retention rules and the financial impact of a wrong total.
The evaluation set should contain ordinary invoices, scans, handwritten notes, multi-page documents, credit notes, missing fields, conflicting totals and examples from each major supplier. Experts should verify every reference field and mark when the correct behavior is “not present” or “needs review.”
Shortlist one proven document-capable model, one higher-capability multimodal model and one lower-cost document option. Score field accuracy, schema validity, abstention on unreadable pages, processing time, throughput, per-invoice workflow cost and the rate of manual corrections. A model that extracts more fields but silently invents missing totals should fail the safety criterion even if its average score is higher.
Finally, test the complete pipeline: malware scanning, OCR or vision input, extraction, validation rules, duplicate detection, accounting-system write-back and human approval. The selected model is the one that meets the invoice process’s quality and control requirements at sustainable peak cost, not the one with the most impressive general benchmark.
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11. Document the decision and monitor it after launch
Keep a decision record that another team can audit or reproduce. At minimum, capture:
- Business owner, technical owner and approval date.
- Task definition, modalities, users, volume, latency target and error severity.
- Model name, exact version, deployment pattern and configuration.
- Evaluation-set construction, reference ownership, metrics, slices and holdout results.
- Observed quality, latency, throughput, reliability and total-cost results.
- Privacy, security, regional, explainability and human-oversight controls.
- Known limitations, accepted exceptions, rollback plan and escalation path.
- Monitoring frequency, alert thresholds, reviewer sampling and the person responsible for action.
Re-evaluate when the model version, provider terms, pricing, region availability, prompt, retrieval index, tool API, input distribution, business process or legal requirement changes. Monitor live quality with sampled human review, drift in inputs and labels, severe-error counts, latency, failures, spend, refusals and escalation volume. Keep enough request and response metadata to investigate incidents without retaining unnecessary sensitive content.
12. Common selection mistakes and how to correct them
Choosing from a public leaderboard alone
Why it fails: Broad tests may not represent your language, documents, policies, tools or error costs.
Correction: Use public benchmarks only for initial screening, then run the validated task set and inspect failures.
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Why it fails: Extra capability may not improve your task enough to justify cost, latency or operational complexity.
Correction: Test a lower-cost or specialized candidate against identical requirements and retain the larger model only when its measured benefit matters.
Optimizing token price instead of workflow cost
Why it fails: Retries, long prompts, retrieval, human correction and tool failures can dominate the bill.
Correction: Calculate cost per completed outcome under realistic volume and include infrastructure and operations.
Testing only ideal examples
Why it fails: Production contains missing data, malformed files, ambiguous requests and edge cases.
Correction: Preserve the real input distribution and add expert-reviewed failure cases, including cases that must be escalated.
Ignoring version and configuration drift
Why it fails: A provider update, changed prompt, retrieval index or tool schema can alter behavior.
Correction: Pin versions where possible, log configuration, retain a holdout suite and trigger regression tests on every material change.
Assuming RAG or fine-tuning fixes every quality problem
Why it fails: Poor retrieval, weak references, noisy training data or missing authorization can make the system worse.
Correction: Diagnose the failure first, test the simplest intervention and evaluate the full application rather than the model in isolation.
13. A reusable decision checklist
- The task, modality, output schema, users and operating context are explicit.
- Volume, concurrency, latency, availability and context requirements are measured.
- Correctness, completeness, grounding, safety, readability and abstention criteria are defined.
- Unacceptable errors and affected-group analyses are identified where relevant.
- The evaluation set reflects ordinary traffic, edge cases and verified references.
- At least one proven, one capable and one economical or specialized candidate were compared when viable.
- All candidates used equivalent prompts, context, tools, filters and test conditions.
- Quality, severe failures, latency, throughput, reliability and workflow cost were recorded.
- Managed, platform and self-hosted options were assessed against data, security, region and team capability.
- Prompting, RAG, fine-tuning and agent designs were chosen based on measured gaps.
- Privacy, explainability, regulatory scope, human oversight and monitoring are documented.
- The model version, evidence, trade-offs, owner and re-evaluation triggers are recorded.
The result is a defensible selection: a model and application design proven on the work your organization actually performs, with controls for the failures that matter and a plan for change.
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