GPT-3 vs GPT-4: architecture, scale, and what actually changed
Oct 27, 2023
A working comparison of GPT-3 and GPT-4: dense vs mixture-of-experts, context length, training data, post-training, and what the differences mean in…
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LLM Selection Pack
Build the LLM eval suite and risk-and-comparison report your approval committee can re-run on every model swap, scored on your own tasks.
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Name the decision
Approval, procurement, audit, and regulated-workflow review all ask the same thing: where is the evidence? A working AI system is necessary but not sufficient: the eval reports, model comparisons, lineage, and approval workflow around it are what unblock the deal. We design and build those artefacts on your candidate models and your tasks, without claiming a certification we are not in the business of issuing.
Three Things Land at the End
What You Keep
The pack is for an approval committee, procurement team, audit owner, or regulated-workflow review that needs structured evidence to decide between candidate LLMs. It runs on your own tasks, not generic vendor benchmarks. There is a strict entry gate: a candidate model list, a task set, representative inputs, and a named approval owner. If any of those is missing we help you assemble it before the pack starts. It runs 3–6 weeks, fixed-price.
The Eval Suite
Programmable
Task definitions, scoring rubric, slice metrics, and a paired-comparison protocol on your candidate models and your tasks.
The Risk & Comparison Report
Decidable
Model-vs-model results, failure modes, lineage notes, and recommended approval conditions a committee can sign against.
The Re-Run Script
Re-runnable
Runs the eval suite end-to-end on the supplied inputs, so the next model swap is a rerun, not a fresh engagement.
What the Eval Suite Is
For the buyer, the eval suite is a programmable harness an approval committee can demand a re-run from at any time: task definitions tied to the work the LLM is being approved for, not generic benchmarks copied from a vendor deck; a scoring rubric the committee can read and challenge; slice metrics that surface where a candidate underperforms; a paired-comparison protocol so model-vs-model results are decidable rather than narrative; lineage notes; and recommended approval conditions, aligned to recognised public frameworks where applicable. The report is its first output; every subsequent rerun is one more.
What This Pack Covers
Task-Specific LLM Evals
Model-Comparison Evidence
Scoring Rubrics
Slice Metrics
Paired-Comparison Protocols
Lineage Notes
GenAI Model-Risk Reports
Approval-Condition Recommendations
Not Sure This Is the Right Pack?
If a deployed AI system has quality regressions or no release gate, that is the Production AI Monitoring Harness. If the question is "score our programme against NIST AI RMF or ML Test Score", that is the AI Readiness Scorecard. If the chosen LLM is too expensive or slow to serve, that is the Inference Cost-Cut Pack; if it does not yet run on the target you need, the AI Porting & Deployment Pack.
How We Know This Works
Model-comparison reasoning, RAG architecture, and LLM-lifecycle discipline. These pieces pre-date the packaged pack and stand as bridged proof.
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A methodology for designing a task-specific LLM eval against your actual workflow that produces the evidence pack a procurement committee can defend.
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An LLM evaluation framework is five layers — task definition, dataset, scoring, run conditions, evidence capture
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Turning an LLM Evaluation Into Sign-Off-Grade Evidence: A Procurement Team's Checklist
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How a procurement team converts raw LLM evaluation results into a defensible evidence artefact that survives an approval committee in one round.
Read moreClient Testimonials
TechnoLynx delivered the project on time and provided quality outputs that met the client's expectations. The team was proactive in providing ideas and suggestions, and they were careful at properly planning the tasks. The client also praised the team's expertise in GPU programming and AI.
TechnoLynx's skill in low-level software development was impressive. TechnoLynx was able to create four prototypes with common components and an interface for easy maintenance. The client was extremely happy with the solution's speed. Moreover, their communication was seamless and straightforward.
TechnoLynx's unique aspect is that they're able to transform complex theories into practicable and applicable results. TechnoLynx provides research reports and architecture planning documents. The team is able to transform complex theories into practicable and applicable results. TechnoLynx's project management is strong and delivers work on time without hardware issues, being responsive through virtual meetings.
I’m delighted with our collaboration with their team. Thanks to TechnoLynx's work, the client has been able to co-author two patents. They lead responsive project management to solve problems quickly. The team also praises their skilled and knowledgeable team.
We had high-efficiency meetings. TechnoLynx’s work resulted in a successful breakthrough, and their input improved the client’s app. Their flexible and organised project management cultivated a healthy collaboration experience. Ultimately, their professionalism and commitment were impressive.
LLM Selection Pack FAQ
How is this different from validating a deployed system?
The Selection Pack produces a paired-comparison harness on your own tasks so an approval committee can decide between candidate LLMs: selection, swap, or approval-to-deploy. Validating an already-deployed system for regressions and release gates is the Production AI Monitoring Harness. The two share eval-harness DNA, not scope.
What does the eval suite actually contain?
Task definitions tied to the work the LLM is being approved for (not generic vendor benchmarks), a scoring rubric the committee can read and challenge, slice metrics, a paired-comparison protocol so model-vs-model results are decidable, lineage notes, and recommended approval conditions, aligned to recognised public frameworks (HELM-style, lm-eval-harness conventions) where applicable.
Can the committee demand a re-run on the next candidate model?
Yes. The reproducible re-run script runs the eval suite end-to-end on the supplied inputs, so the next time a vendor revs the model, a prompt changes, or a new candidate enters the shortlist, the team triggers a rerun rather than a fresh engagement.
Do you certify the model or give regulatory advice?
No. The output is the structured evidence your buyer, auditor, or compliance owner signs against, not a certification, legal advice, or regulatory interpretation. AI security red-teaming as a primary deliverable is not currently productised here.
How is this different from readiness scoring against a rubric?
The AI Readiness Scorecard reports against a named published external rubric; the Selection Pack produces a paired-comparison harness on your own tasks. One scores a programme against an external reference; the other decides between candidate models on the work they will actually do.
Start a Conversation
The AI-infrastructure / SaaS crosswalk routes LLM-eval and approval-evidence work through this pack. For the wider discipline this pack delivers, see AI governance and trust. For the broader argument about how benchmark methodology should work, see LynxBenchAI, our benchmark sub-brand on this same origin, which ships as a benchmark you can install and run against your own hardware. The Selection Pack is the TechnoLynx engagement buyers commission against their own LLMs and tasks.
If you have a candidate LLM shortlist, a task set you can describe, representative inputs, and an approval owner, contact us and tell us the candidate models, the task shape, and what your approval workflow needs the evidence to look like.