I use LLMs as a staff engineer in 2026
In 2026, staff engineers increasingly rely on advanced LLM agents to generate complete pull requests and diagnose most bugs, marking a shift from earlier uses like autocomplete and one-off code queries. Agents now operate effectively across multiple repositories and require only light human review, though engineers still oversee and validate outputs. Human expertise remains critical, especially in complex debugging and high-level communication, where LLMs struggle with concision and contextual understanding.
- ▪LLMs now generate entire pull requests in familiar codebases, with engineers typically making only one editing pass before merging.
- ▪Agents diagnose about 80% of bugs independently, especially when given access across multiple repositories.
- ▪Engineers still write their own PR descriptions to better convey the core idea and signal personal review.
- ▪Current agents recover from errors autonomously and operate too quickly for real-time human intervention.
- ▪Human insight remains essential in narrowing down complex bugs, even when an agent ultimately identifies the issue.
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| Original publisher | Seangoedecke |
| Canonical URL | https://www.seangoedecke.com/how-i-use-llms-in-2026/ |
| Publication time | Sat, 16 May 2026 22:01:32 +0000 |
| Retrieval time | 2026-05-16T22:10:19.058Z |
| Last seen | 2026-05-16T22:10:19.058Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _B40QoREG63Q |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
How I use LLMs as a staff engineer in 2026A bit over a year ago I wrote How I use LLMs as a staff engineer. Here’s a brief summary of what I used AI for last year: Smart autocomplete with Copilot Short tactical changes in areas I don’t know well (always reviewed by a SME) Writing lots of use-once-and-throwaway research code Asking lots of questions to learn about new topics (e.g. the Unity game engine) Last-resort bugfixes, just in case it can figure it out immediately Big-picture proofreading for long-form English communication Here are some tasks I explicitly didn’t use AI for last year: Writing whole PRs for me in areas I’m familiar with Writing ADRs or other technical communications Research in large codebases and finding out how things are done February 2025 was a long time ago.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Seangoedecke.
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