This index exposes the material behind every decision guide: what we reviewed, when we retrieved it, the claim it supports and where the evidence stops.
158decision guides
707source records
699scoped claim notes
How to read this index
A citation is not a transferable result.
Reviewed identifies the part of the source used. Supports states the bounded proposition we draw from it. Retrieved records when time-sensitive material was checked.
Vendor documentation establishes vendor claims and product behavior as documented, not independent performance. Papers establish results only for their stated methods and tested scope. AccessAllGPT recommendations remain labeled analysis.
AccessAllGPT desk review of Anthropic engineering guidance, OpenAI’s agent-building guide and NIST AI 600-1, retrieved 2026-08-08. Vendor definitions and recommendations are labeled as such; the decision framework, gates and templates are AccessAllGPT guidance. This is not an original benchmark, agent trial, cost study, security assessment or vendor comparison.
Reviewed: Definitions; when and when not to use agents; frameworks; augmented LLMs; workflow patterns; autonomous agents; evaluation and production considerations
Supports: Anthropic distinguishes workflows with predefined code paths from agents whose process and tool use are directed dynamically by a model, recommends the simplest sufficient design, and describes latency, cost, control and evaluation trade-offs. These are vendor-authored engineering recommendations, not comparative trial results.
Reviewed: Pages 4–8, 13–17 and 24–32: agent definition, use-case criteria, design foundations, orchestration, guardrails and human intervention
Supports: OpenAI defines agents as systems in which an LLM manages workflow execution and dynamically selects tools, says deterministic solutions may suffice outside complex or ambiguous use cases, and recommends incremental orchestration, layered guardrails and human intervention. These are vendor-authored recommendations, not independent evidence of production performance.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation. It does not certify an agent or prescribe the AccessAllGPT decision framework.
AccessAllGPT desk review of NIST AI 600-1, current OpenAI API data-control documentation, vLLM stable engine configuration documentation and Kubernetes GPU-scheduling documentation, retrieved 2026-08-07. Vendor statements, implementation documentation and AccessAllGPT guidance are separated. This is not an original benchmark, cost study, security assessment or legal review.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Reviewed: Data use, abuse-monitoring retention, application state, retention controls and data residency
Supports: OpenAI states that API data is not used to train its models unless the customer opts in, describes default abuse-monitoring retention of up to 30 days, and documents feature- and eligibility-dependent retention controls. These are vendor statements about one managed service, not findings about every API or account.
Reviewed: Model, load, parallel, cache, device, scheduler and observability configuration groups
Supports: The serving engine exposes choices including model and tokenizer resolution, data type, quantization, model length, tensor and pipeline parallelism, GPU memory utilization, CPU offload and observability. This establishes configuration surface, not a universal performance or cost result.
Supports: Kubernetes documents vendor drivers and device plugins as prerequisites for GPU scheduling, GPU resources specified through limits, and node labels or affinity for selecting accelerator types. It describes a deployment mechanism, not a complete inference operating model.
AccessAllGPT desk review of the original RAG paper, NIST AI 600-1 and current OpenAI implementation guidance, retrieved 2026-08-06. Paper findings, vendor guidance and AccessAllGPT recommendations are labeled separately. This is not an original benchmark, cost study or product comparison.
arXiv (authors from Facebook AI Research, University College London and New York University) · Retrieved
Reviewed: Abstract, introduction, methods, experiments, results, discussion and limitations of the paper's experimental scope
Supports: The authors define and evaluate a specific RAG architecture that combines parametric sequence-to-sequence memory with retrieved non-parametric Wikipedia passages; this supports the architectural distinction, not a universal claim that retrieval improves every application.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Reviewed: LLM optimization context, prompt engineering, retrieval-augmented generation, fine-tuning and production accuracy sections
Supports: Vendor-authored guidance distinguishes context optimization for missing, stale or proprietary knowledge from model optimization for inconsistent format, style or behavior, and recommends an evaluate–hypothesize–change–evaluate cycle.
Reviewed: Overview, stated use cases, evaluation prerequisite, dataset construction and data-format guidance
Supports: Vendor-authored product guidance describes supervised fine-tuning with prompt-and-response examples for classification, translation, specific output formats and instruction-following behavior, and says to establish evaluations before investing in fine-tuning.
AccessAllGPT desk review of NIST AI 600-1, the HELM v2 paper, the original Chatbot Arena paper and OpenAI evaluation guidance, reverified 2026-08-06. Historical study findings, vendor guidance and AccessAllGPT recommendations are separated below. This is not an original benchmark, product test or vendor ranking.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
Transactions on Machine Learning Research / arXiv · Retrieved
Reviewed: Abstract, methodology summary and reported evaluation scope
Supports: The authors define scenario coverage and multi-metric evaluation as distinct parts of model evaluation, and report standardized evaluation across accuracy, calibration, robustness, fairness, bias, toxicity and efficiency.
arXiv (authors from LMSYS Org and UC Berkeley) · Retrieved
Reviewed: Abstract, methodology description and stated validation claims
Supports: The authors describe Chatbot Arena as pairwise, crowdsourced human-preference evaluation and report agreement checks against expert raters; this establishes what the published ranking measures, not workload-specific production fitness.
Reviewed: What are evals, design your eval process, evaluator types and edge cases
Supports: Vendor guidance to define an evaluation objective, collect representative and edge-case data, combine evaluator types and continuously evaluate; used as implementation guidance rather than independent evidence of an OpenAI product claim.
AccessAllGPT synthesis of primary standards, security guidance, platform controls and benchmark methodology. This is not an original benchmark and does not score any vendor.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.
National Institute of Standards and Technology · Retrieved
Reviewed: Publication abstract, scope, citation and report metadata
Supports: NIST describes the profile as a voluntary, cross-sector companion to AI RMF 1.0 for incorporating trustworthiness considerations into design, development, use and evaluation.
This earlier source record links the primary material but does not yet carry a section-level claim note. Consult the article’s methodology and limitations before transferring a claim.