Alex is the co-founder and CEO of Patentext. He’s spent over a decade drafting patents for startups, unicorns like Uber and Dropbox, and everything in between. When he’s not obsessing over Patentext or running his climate tech-focused IP firm, he’s likely training for a triathlon or chasing a very fast border collie.
OpenAI introduced GPT-5 on August 7, 2025. Within days, a familiar pattern repeated across startup Slack channels and founder communities: "Can I use GPT-5 to write my patent?"
It's a reasonable question. GPT-5 produces more coherent long-form text than its predecessors, handles technical concepts better, and can generate surprisingly plausible-sounding legal documents. And patent applications are expensive—a professionally drafted provisional can cost $3,000–$7,000, while a non-provisional runs $8,000–$18,000 before prosecution costs.
But "plausible-sounding" and "legally defensible" are different things. Here's an honest assessment of what GPT-5 and similar LLMs can and can't do for patent drafting in 2025.
What GPT-5 can do reasonably well
Explaining patent concepts
GPT-5 is genuinely useful for founders who are new to patents and need to understand the landscape. It can explain the difference between provisional and non-provisional applications, describe what patent claims are, summarize what prior art means, and provide an overview of the patent prosecution process. For educational purposes, it's accessible and reasonably accurate on well-established legal concepts.
Drafting background sections
The background section of a patent application describes the technical field and the problem the invention solves. This section is largely descriptive and doesn't require the same legal precision as claims. GPT-5 can produce a decent draft of a background section if given sufficient information about the invention.
Generating a first-pass detailed description
The detailed description section explains how the invention works. GPT-5 can help structure this section and convert a founder's technical explanation into a more formal description. This output typically requires significant editing to be legally useful, but it can accelerate the drafting process.
Identifying potential prior art categories
If you describe an invention to GPT-5 and ask it to identify what kinds of prior art might exist, it can suggest relevant technical areas and search terms. This isn't a substitute for a professional prior art search, but it can help orient an inventor before doing their own initial research.
Where GPT-5 fails for patent drafting
Claims drafting
This is the most important part of a patent application and the place where LLMs consistently fall short. Claims are the legally operative part of a patent—they define exactly what the patent owner can exclude others from doing. Good claims require:
- Precise, unambiguous language developed through decades of case law
- Strategic scope decisions: claims too broad will be rejected; claims too narrow provide weak protection
- Understanding of how examiners will interpret claim language
- Anticipating how competitors might try to design around the claims
- Knowledge of what's actually in the prior art and what distinguishes the invention
GPT-5 can generate text that looks like patent claims. It cannot make the strategic judgment calls that determine whether those claims will survive examination or hold up in litigation. In our experience testing various LLMs on patent claims drafting, the output typically requires complete rewriting by a patent professional—not editing, rewriting.
Prior art analysis
GPT-5 has a knowledge cutoff and no ability to search live patent databases. It cannot tell you whether a specific claim element is anticipated by an existing patent. It may cite patents that don't exist or mischaracterize patents that do. For anything legally consequential, don't rely on LLMs for prior art research.
Jurisdiction-specific requirements
Patent law varies significantly across jurisdictions. USPTO requirements differ from EPO requirements, which differ from national offices. Recent case law—Alice, Mayo, and their progeny—continues to evolve and requires current knowledge to navigate. GPT-5's training data has a cutoff, and patent law changes frequently enough that this matters.
Invention disclosure and strategy
Before you can draft a patent, you need to identify what's actually novel about your invention. This requires a structured conversation that surfaces technical details, distinguishes what you've built from the prior art, and identifies the strategic scope of protection you're seeking. GPT-5 can participate in such a conversation but lacks the patent expertise to guide it effectively.
The GPT-5 comparison (and why it matters for how you think about AI drafting)
GPT-5 is the best general-purpose LLM available at time of writing for patent-related tasks. It outperforms GPT-4 and Claude 3.5 on coherence and technical accuracy in our informal testing. But for patent applications specifically, general capability isn't the constraint—domain-specific judgment is.
Patent claims require knowledge of prosecution strategy, case law, and examiner behavior that general-purpose LLMs don't have. The gap isn't about the quality of the language model; it's about whether the model has been trained and fine-tuned on the specific task of writing defensible patent claims.
What actually works: AI + patent agent review
The effective approach isn't "use GPT-5 to write your patent" or "ignore AI and hire a law firm." It's using AI for the parts where it works (structuring disclosure, drafting descriptions, prior art search orientation) while having a trained patent professional handle the parts where it doesn't (claims strategy, prosecution decisions, legal judgment calls).
This is the model Patentext uses: structured invention disclosure through an AI-assisted interface, AI-generated draft applications that are then reviewed and finalized by a patent agent before filing. The result is professional-quality output at a fraction of traditional law firm pricing—not because we've replaced human judgment, but because we've used AI to reduce the time a patent agent needs to spend on each application.
Disclaimer: This article is for informational purposes only and does not constitute legal advice. Patent laws are complex and vary by jurisdiction. For personalized guidance, consult a qualified patent attorney or agent.