Evaluating Legal AI Tools: A Practical Testing Framework

Before you roll out any legal AI tool across a firm, you need more than a slick demo. You need to know:

what it actually does well;
where it breaks; and
how it behaves on real work, not marketing examples.

This article sets out a practical, low-drama evaluation framework for UK firms testing large language model (LLM) tools – whether that is an AI assistant built into your case management system or a standalone product.

The aim is not to produce a 60-page benchmarking report. It is to give partners and IT/risk teams a shared, repeatable way to say “yes”, “no” or “not yet”.

1. Start with real use cases, not generic prompts

Avoid abstract tests like “write an essay about unfair dismissal”. Instead, pick 3–5 real scenarios that matter to your firm, for example:

summarising a County Court order and explaining next steps to a lay client;
turning a messy email chain into a chronology with action points;
drafting a first-pass letter before action in a standard type of claim;
suggesting missing issues in a draft advice or skeleton.

For each use case, define:

Input – what documents or text will you provide?
Task – in one sentence, what should the tool do?
Audience – who is the output for (partner, junior, client, court)?
Constraints – what must it not do (invent cases, give unqualified advice, exceed a word limit, etc.)?

These become your test cards. Every tool gets measured against the same set.

2. Create “golden answers”

For each test card, prepare a short example of a good answer, written by a competent human in your firm.

It does not need to be perfect, but it should:

be factually and legally accurate;
use a tone appropriate for the audience; and
follow your house style on headings, disclaimers and risk language.

These golden answers give you a reference point. When the tool produces output, reviewers can ask:

Is this better, worse or about the same as the golden answer?
If it is worse, why – hallucinations, omissions, poor tone, structure?

Without golden answers, evaluations often degenerate into “I like this” vs “I don’t”.

3. Decide what “good enough” means in advance

Before you run any vendor through your test cards, agree internally:

What does “pass” mean for each use case?
Where is “OK with supervision” acceptable, and where is “near perfect” required?
Which failures are absolute deal-breakers (for example, invented authorities in research tasks)?

You might, for example, decide that:

for internal research orientation, you will accept an output that is 70–80% useful provided authorities are always checked;
for client-facing drafts, you want good structure and tone, but detailed content will always be edited by a solicitor;
for any task touching court submissions, you have zero tolerance for hallucinated cases or misquotations.

Writing these thresholds down makes tool comparisons much less subjective.

4. Run structured tests with multiple reviewers

When you test a tool:

Give it the same set of test cards you used for other tools.
Ask several reviewers (trainees, associates, partners) to score each output against the golden answer.
Capture scores and comments in a simple spreadsheet.

Useful scoring dimensions include:

Accuracy (1–5) – are facts and law correct? Any hallucinations?
Completeness (1–5) – are key points covered, or are there gaps?
Clarity and structure (1–5) – could a busy fee-earner or client understand this quickly?
Editing effort (1–5) – how much work would be needed to make this sendable?

Encourage reviewers to annotate specific problems (“misstates test”, “misses limitation point”, “excellent structure but thin on detail”) rather than just assigning numbers.

5. Check non-functional aspects: safety, logging and UX

Even a tool with good outputs may be unsuitable if it fails basic governance checks. Your evaluation should also cover:

Data handling – where is data stored and processed? Are prompts/outputs used to train general models?
Security – SSO, access controls, logging, incident response.
Auditability – can you see which prompts were run and by whom for a given matter?
Configuration – can you turn features on/off, restrict certain tasks or models, and set sensible defaults?

The best text output in the world is not worth a regulator’s letter asking awkward questions about your providers.

6. Pilot with real matters before firm-wide rollout

Once a tool passes structured testing, run a time-limited pilot:

choose a few teams and matter types;
collect feedback on usefulness and failure modes;
track simple metrics (time saved, reduction in write-offs, speed to first draft).

Make it explicit that:

partners remain responsible for the work;
AI-assisted outputs must be saved to the matter file; and
hallucinations or other problems should be reported, not quietly ignored.

After the pilot, decide whether to:

roll out more broadly;
confine the tool to specific use cases; or
park it and revisit later when the technology improves.

Where OrdoLux fits

OrdoLux is being built with this kind of evaluation in mind:

AI features are designed around concrete workflows (research, communication, chronologies), not generic chatboxes;
prompts and outputs live inside the matter file, making supervision and auditing easier; and
firms can plug in approved models and providers behind a consistent interface, instead of juggling multiple separate tools.

That way, you can evaluate “OrdoLux + chosen model” using the same test cards you apply to any other platform.

This article is general information for practitioners — not legal advice.

Looking for legal case management software?

OrdoLux is legal case management software for UK solicitors, designed to make matter management, documents, time recording and AI assistance feel like one joined-up system. Learn more on the OrdoLux website.