GPT-6.1 Sol Drafts, Your Fact Sheet Decides

01 · Where Proposal Facts Break Under Team Volume

At one or two proposals a week, a wrong delivery date is a typo. At team volume, it becomes a pattern. When a sales organization pushes dozens of proposals through a model every month, the failures stop looking random: a legacy price reappears in three documents, a retired service tier creeps back into an appendix, and a discount promise written for one region turns up in another. The cause is rarely the model's reasoning. It is the absence of a controlled input. Models are fluent, and fluency hides gaps. A confident paragraph about implementation timelines reads exactly like a paragraph about timelines that actually came from a signed statement of work.

Team leads inherit the problem as rework, late legal reviews, and credibility damage that no second draft fully repairs. The fix is procedural rather than rhetorical. Decide what the model is allowed to know, pin every factual claim to a place in that material, and build the review loop before anyone generates a single page. The goal of this primer is a repeatable pipeline: a team running heavy, sustained drafting workloads should be able to hand a new hire the process and get the same factual discipline on their first week of output.

02 · The Fact Sheet That Runs the Whole Funnel

Before any drafting begins, assemble one approved fact sheet and give it a version number and an owner. This document is the only admissible knowledge for factual claims. It should carry the items your proposals most often get wrong:

• › Pricing and packaging: current list rates, standard discount bands, minimum terms, and anything explicitly retired.
• › Scope language: approved descriptions of what is included, what is optional, and what is out of scope.
• › Delivery and support: standard implementation windows, support tiers, escalation paths, and service commitments you are actually authorized to offer.
• › Technical claims: named integrations, platform requirements, data handling statements, and compliance wording cleared for external use.
• › Regional and legal constraints: which clauses apply where, and which terms need legal review before they appear anywhere.

Each line gets an owner and a review date. When something changes, you update the fact sheet first and only then regenerate affected sections. This single habit converts fact accuracy from a per-document effort into a per-update effort. It also gives your review team a fixed artifact to check against, which is far faster than arguing with a draft paragraph by paragraph.

03 · Assign Model Depth to Each Proposal Stage

Not every stage deserves the same treatment. Early funnel work tolerates more variation; late funnel work is nearly contractual. Split the pipeline accordingly. Requirement extraction, call-note summarization, and first outlines can run on a lighter, faster model configuration, because any error there is caught downstream by the fact sheet and the review gate. The main narrative, pricing narrative, and executive summary should run on your strongest drafting configuration, where reasoning quality shows up in structure and tone as well as accuracy.

A pairing that works well for sustained teams is Claude Sonnet 5.5 for the main proposal draft and GPT-6.1 Sol for a skeptical second pass that reads the draft against the fact sheet and flags claims it cannot trace. Two roles, two passes, one rule: the model that audits must receive a changed brief, not the same prompt that produced the draft. Otherwise you are asking a system to confirm its own habits.

Keep the stage assignment explicit in your team documentation. When someone asks which configuration handles pricing appendices, the answer should be a line in a table, not tribal knowledge. Explicitness also protects your budget: heavyweight drafting calls are spent only where the prose will actually reach a customer.

04 · Scaffolding Prompts So Every Number Has a Home

The strongest lever on fact accuracy is prompt structure. A reliable drafting brief has a fixed shape, and every proposal call follows it:

• › Role and audience: who the proposal is for, what decision it must support, and the tone standard.
• › The fact sheet block: the approved material, version-stamped, placed before any instructions that depend on it.
• › Required fields: named sections with explicit expectations, so nothing important is improvised at the end of a long document.
• › Traceability instruction: each factual claim must be supported by a line in the fact sheet; anything unsupported must be written as a labeled placeholder rather than a plausible guess.
• › Prohibition on outside knowledge: no market statistics, competitor claims, customer names, or pricing that does not appear in the approved block.
• › Output checks: a closing self-check listing every number, date, and named commitment the draft contains, so a reviewer can audit the list rather than the prose.

Placeholders matter more than they sound. A visible gap like an unfilled delivery window is easy to escalate; a smoothly written invented one is not. Teach your team that a bracket in the draft is a success signal, not a failure, and the model's tendency to fill gaps stops being dangerous.

Apply the same discipline to context you feed in. Customer call notes, discovery answers, and prior proposals are useful, but each is a potential source of stale facts. Summarize them into the fact sheet structure before drafting rather than pasting raw transcripts and hoping the model distinguishes history from current policy.

05 · Token Budgets, Caching, and a Review Loop That Scales

Sustained proposal work is a token economics problem as much as a quality problem. The same fact sheet is sent on every call, and the same section patterns recur across dozens of documents. That repetition is where a metered relay pays off. Quanta is a pay-as-you-go API relay built for power users and teams with heavy, sustained workloads: one balance covers Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, Grok and other models, and it works with Claude Code, Codex, Gemini CLI and any OpenAI SDK. Every call is metered on real tokens, with cache writes and cache reads priced separately, which rewards the exact pattern described above. Keep the stable fact sheet block in cache and pay the lower read cost on every subsequent draft instead of resending the full context at write cost.

Budget control follows the same logic as fact control: centralize it. Track spend per team, set a review cadence for heavy drafting periods, and treat cache behavior as a metric worth watching alongside accuracy. If cache reads dominate, your context is stable and your pipeline is healthy. If you keep paying write costs on unchanged material, your prompts are drifting, and drift usually arrives hand in hand with factual inconsistency.

Close the loop with a human gate that is cheap to run. Before a proposal leaves the team, a reviewer checks the self-check list of numbers and commitments against the fact sheet version cited in the prompt, confirms every placeholder is resolved or escalated, and logs the fact sheet version used. That log turns post-mortems into evidence: when a claim is wrong, you can see whether the fact sheet was stale, the prompt was loose, or the review slipped.

Run this pipeline consistently and model-assisted proposals stop being a gamble on fluency. The draft becomes fast, the facts become checkable, and the team spends its attention on strategy rather than proofreading. When your team is ready to run this loop on sustained volume, open a Quanta account and put the whole pipeline behind one metered balance.