I evaluated my self-trained LLM what 31% accuracy actually means
The author evaluated their self-trained language model, achieving a 31% accuracy on a test set of 200 questions. This performance is better than random guessing but significantly lower than advanced models like GPT-4. The author emphasizes the importance of sharing honest evaluation results to provide transparency in AI projects.
- ▪The model achieved 31% accuracy, outperforming random guessing by 6 percentage points.
- ▪Compared to GPT-4, which scores around 90%, the author's model shows room for improvement.
- ▪The author suggests that using a larger base model and better knowledge sources could enhance performance.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/yakhilesh/5-i-evaluated-my-self-trained-llm-what-31-accuracy-actually-means-4ljf |
| Publication time | Sat, 16 May 2026 08:59:49 +0000 |
| Retrieval time | 2026-05-16T09:10:17.965Z |
| Last seen | 2026-05-16T09:10:17.965Z |
| 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 | mZLx6KOVHnW2 |
| 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 |
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| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1358056) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Akhilesh Posted on May 16 I evaluated my self-trained LLM what 31% accuracy actually means #ai #sideprojects #productivity #beginners Most AI projects don't include evaluation. They show a nice demo, pick cherry-picked examples, and call it done. I wanted to be honest, so I tested my model on 200 questions it had never seen. How I evaluated The test set has 1,273 questions that were never used in training.
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