Reducing the cognitive load of AI changes
The article discusses the cognitive burden developers face when reviewing AI-generated code that uses unfamiliar terminology. To mitigate this, the author employs a pre-review process where the AI extracts and documents bespoke terms for human confirmation. This workflow allows for consistent renaming of abstractions, resulting in code that aligns better with the developer's mental model and is easier to understand.
- ▪Developers experience increased cognitive load when AI-generated code uses terminology that does not match their personal naming conventions.
- ▪The author uses a specific prompt to have the AI identify and document unconventional terms before the final code review.
- ▪This process involves the developer confirming or suggesting alternative names for the identified abstractions.
- ▪The AI then performs a global find-and-replace operation to update the code and documentation with the approved terms.
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Record
| Original publisher | Github |
| Canonical URL | https://amoffat.github.io/blog/cognitive-load.html |
| Publication time | Thu, 01 Oct 2026 21:08:26 +0000 |
| Retrieval time | 2026-10-01T21:12:55.190Z |
| Last seen | 2026-10-01T21:12:55.190Z |
| 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 | D7EvNcYrCQ21 · 1 stories |
| 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
Reducing the cognitive load of AI changes Andrew · 1 October 2026 · 2 min read When reviewing large amounts of AI-generated code, I often find that the LLM chooses terms for abstractions that do not always map to my own choices. For instance, what it may call a MutationIntent might personally be more natural to me as an EditRequest. Because the LLM's choice of words is not my ideal choice, I have to do a mental lookup of what it means every time I see it, which adds cognitive load. This may seem like a small friction, but the cognitive load accumulates when considering how dozens of new terms interact in unfamiliar code. I can only hold a finite number of these semantic lookups in my head before I start misinterpreting how things work.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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