Claude 2's 100K Context Window: Advanced Document Analysis For Executive Decisions

Reading a 200-page due diligence report cover-to-cover is not a good use of an executive's time. Neither is asking an analyst to spend two days summarizing it into a deck. On July 11, 2023, Anthropic released Claude 2, and the headline feature is a 100,000-token context window. That is roughly 75,000 words in a single prompt, which is enough to hold an entire report, a vendor contract, and the addenda alongside it. For executives who deal in dense documents, that is genuinely useful.

What happened with Claude 2

The release came with meaningful performance upgrades across reasoning, coding, and math. But for most executives, the benchmark numbers matter less than the specific scores: Claude 2 hit 76.5 percent on the multiple-choice section of the Bar exam, up from 73.0 percent for Claude 1.3. It scored above the 90th percentile on GRE reading and writing. On Python coding evaluations (Codex HumanEval), it went from 56.0 to 71.2 percent. On grade-school math (GSM8k), it scored 88.0 percent.

Those aren't the numbers of a novelty tool. They're the numbers of a competent analyst-level assistant.

Access comes through two paths. The public beta at claude.ai is free for users in the United States and UK. The developer API is priced the same as Claude 1.3: $8.00 per million input tokens and $24.00 per million output tokens. The web interface supports direct file uploads, up to five files at a time, including PDFs, CSVs, and plain text. You can drag a contract in and start asking questions without any technical setup.

Why this matters for executives

Before this, working with long documents required chunking: breaking a report into small sections, feeding each piece into a model separately, summarizing each chunk, then manually stitching the results together. That process is slow. It also strips context, because what's in section 4 often only makes sense in light of what section 9 says. You lose the cross-document connections that matter most in due diligence or contract review.

With a 100,000-token window, those documents go in whole. For due diligence, you can upload five target-company reports and ask the model to surface conflicting financial figures or hidden liabilities across all of them. For legal review, a general counsel can load a master services agreement alongside multiple addenda and ask the model to flag inconsistencies in liability clauses. For market analysis, you can compare a competitor brief against an internal strategy deck and ask where the assumptions diverge.

The model uses Constitutional AI, Anthropic's framework for making systems more helpful and less likely to produce harmful outputs. In practice, that means Claude 2 is more cautious and less prone to the kind of confident, damaging nonsense that causes problems in enterprise settings. It's not a perfect filter, but it matters when the output is going to a board or a client.

Action steps for document analysis

Getting this into your workflow this week takes four steps.

First, gather and format your source files. Collect up to five documents tied to a single decision. Competitive analyses, vendor proposals, internal policy drafts, whatever is relevant. Confirm the files are in PDF, CSV, or TXT format, all of which claude.ai handles natively.

Second, log in at claude.ai. If you're working with sensitive proprietary data, talk to IT first. The free public beta has different data usage terms than the paid API. For anything confidential, use the API channel, which does not use your data to train the model.

Third, upload your files using the paperclip icon and run a structured extraction prompt. Generic summary requests produce generic outputs. Use something specific:

Analyze the uploaded documents, which include our draft vendor contract, the vendor security policy, and our internal compliance guidelines. Perform the following tasks:

1. Identify the top three financial risks in the contract, specifically looking for hidden fees, auto-renewal clauses, or unfavorable payment terms.
2. Compare the vendor security policy against our internal compliance guidelines. List any areas where the vendor does not meet our minimum standards.
3. Draft a 200-word executive summary of your findings, followed by three specific negotiation recommendations for our procurement team.

Fourth, verify the outputs. Have someone cross-reference the specific page numbers and figures the model cites against the original documents. This is not optional. AI-generated text is not a final source of truth for legal or financial commitments, no matter how confident the output looks.

Real risks and caveats

Three things can go wrong, and executives should understand all of them before putting Claude 2 into regular use.

The first is what researchers call the "lost in the middle" problem. Large language models tend to recall information near the beginning and end of a long prompt more reliably than information buried in the middle. Claude 2 can ingest 75,000 words, but a critical clause on page 140 of a 300-page document may get less attention than the opening pages. The fix is to run follow-up queries that ask the model specifically about the middle sections, or to split your document into separate queries by chapter.

The second is data privacy. The free public beta at claude.ai allows data use for model improvement unless you opt out. Do not upload non-public financial data, trade secrets, or customer records into the free tool. Use the paid API for anything sensitive.

The third is hallucination. Claude 2 is accurate enough on reasoning tasks to feel authoritative. That makes it more dangerous than a clearly unreliable tool. It can misread a scanned PDF where the OCR text is corrupted, or produce a confident incorrect figure from a data table. Treat the output as a skilled first draft, not a finished analysis. Someone with domain expertise needs to review any document the model produces before it influences a real decision.