Anthropic: Claude Sonnet 4

Review this Python function for potential race conditions. It's meant to update a shared cache from multiple worker threads, but we're seeing occasional data corruption in production.

The function has a classic check-then-act race condition. Between checking if the key exists (line 14) and writing the value (line 18), another thread can modify the same key. You need atomic operations here. Replace the if-check with a threading.Lock context manager around the entire read-modify-write sequence, or switch to a thread-safe data structure like queue.Queue. The current time.sleep() in the update path makes the race window even wider — that's likely why you're seeing corruption under load. Also note that dict operations aren't atomic in CPython despite the GIL; you can't rely on that for correctness.

Demonstrates Claude Sonnet 4's ability to identify concurrency bugs from code context alone, without needing a full reproduction case. The 1M token window means you can paste entire codebases for this kind of review. However, without public benchmarks, we can't quantify its accuracy against HumanEval or similar coding tests.

I have 47 pages of legal discovery documents (PDFs). Extract every mention of 'Acme Corp' or 'Acme Industries', the date of the mention, and whether it's in a contract, email, or memo. Output as CSV.

Claude Sonnet 4 would process all 47 PDFs in a single request (file modality support), then return a structured CSV with columns: Document_Name, Page, Entity_Name, Date_Mentioned, Document_Type, Context_Snippet. Each row represents one mention, with the surrounding sentence for verification. The model handles OCR inconsistencies (e.g., 'Acm e Corp' with a space) and disambiguates 'Acme' references that aren't the target entities. Output includes ~180 rows, sorted chronologically, ready for import into your case management system.

Showcases the 1M token context window and multi-file processing — a realistic legal or compliance workflow that would require chunking and reassembly with smaller models. The $3/$15 per Mtok pricing makes this expensive for high-volume document review compared to batch-optimised alternatives, but the single-pass accuracy is the trade-off.

Explain how RLHF works to a product manager who understands A/B testing but has no ML background. They need to decide whether to invest in it for our chatbot. 3 paragraphs max.

RLHF (Reinforcement Learning from Human Feedback) is like running an A/B test where humans grade the chatbot's answers, then the model learns to favor responses that score higher. Instead of one test, imagine thousands of micro-tests where the model tries different phrasings, gets feedback, and adjusts. Over time, it learns patterns: 'users prefer concise answers' or 'avoid hedging language in support contexts.' The investment trade-off: you need human raters (expensive, slow) and ML infrastructure to retrain the model continuously. If your chatbot handles high-stakes interactions (medical advice, financial guidance), RLHF reduces harmful outputs better than prompt engineering alone. If you're answering FAQs, the ROI is weaker. For your decision: start with a pilot. Collect 500 chatbot interactions, have your support team rate them, and see if the patterns are consistent enough to train on. If raters disagree more than 30% of the time, your feedback signal is too noisy for RLHF to work well.

Highlights Claude Sonnet 4's ability to translate technical concepts into business context without dumbing down the nuance. The image+text modality means you could include architecture diagrams in the prompt for even richer explanations. The response stays within the 3-paragraph constraint while delivering actionable advice, though the lack of benchmark data means we can't compare its explanation quality to GPT-4 or other instruction-tuned models.

When Claude Sonnet 4 handles 200-page RFP reviews in one pass

A 12-person procurement team at a mid-size SaaS company needs to compare vendor proposals that arrive as 150-300 page PDFs with exhibits, pricing tables, and legal riders. Claude Sonnet 4's 1M token context window means the entire RFP stack fits in a single prompt—no chunking, no summary chains, no lost cross-references between sections. At $3/Mtok input, a 200-page review costs roughly $1.50 in tokens, which beats the 4 hours of paralegal time it replaces. The model handles native file uploads, so the team drops PDFs straight into Switchy without preprocessing. If your contracts are under 50 pages or you're running 500+ reviews per month, a cheaper model with retrieval makes more sense. For mid-volume, high-stakes document work where context matters, Sonnet 4 is the call.

Why Sonnet 4 works for support teams under 2,000 tickets/month

A 5-person support team at a B2B fintech startup uses Switchy to auto-tag and route inbound tickets from Intercom. Claude Sonnet 4 reads the ticket text, scans the last 10 messages of conversation history, checks against a 40-page internal knowledge base, and assigns priority + department in under 3 seconds. The image-handling means screenshots of error states get parsed inline without a separate OCR step. At 2,000 tickets/month and an average of 800 tokens per classification, the team spends about $5/month on input tokens. Output is minimal (just tags), so the $15/Mtok rate barely registers. The model's accuracy keeps manual overrides under 8%, which saves 6 hours/week. If you're above 5,000 tickets/month, switch to a faster, cheaper model and accept the 12% override rate.

When Sonnet 4 turns API logs into onboarding guides for dev teams

A 3-person DevRel team at an infrastructure startup needs to write onboarding docs for a new API every 6 weeks. They feed Claude Sonnet 4 the OpenAPI spec (18,000 tokens), 40 example request/response pairs from production logs, and a style guide, then ask for a 4,000-word tutorial with code samples in Python and Go. The model's file-upload support means they drop the YAML spec directly into Switchy; the 1M token window means all examples stay in context so the narrative stays consistent. At $3 input and $15 output per Mtok, one doc costs about $0.15 in tokens and takes 90 seconds to draft. The team edits for 30 minutes and ships. If you're writing 50+ docs per month, the output cost adds up fast—consider a cheaper model and a human editing pass.