How My Career Evolved Like an AI (LLM Architectures )System
The article explores the author's career evolution through the lens of AI architectures. It identifies three phases: education as an encoder, industry work as a decoder, and solution architecture as an encoder-decoder. Each phase reflects a different aspect of learning and application in the context of knowledge work.
- ▪The author compares their career stages to three types of LLM architectures: encoder, decoder, and encoder-decoder.
- ▪In the education phase, the focus was on absorbing and representing knowledge without generating outputs.
- ▪During the industry phase, the author shifted to generating real outputs based on previously encoded knowledge.
- ▪As a Solution Architect, the author combines encoding business requirements with decoding them into technical solutions.
DEV.to (Top) files mainly under programming. We currently carry 4,924 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/sreeni5018/my-journeymy-ai-architecture-125l |
| Publication time | Fri, 22 May 2026 07:20:54 +0000 |
| Retrieval time | 2026-05-22T07:32:00.884Z |
| Last seen | 2026-05-22T07:32:00.884Z |
| 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 | Xai6PqbbagMC |
| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1829954) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Seenivasa Ramadurai Posted on May 22 How My Career Evolved Like an AI (LLM Architectures )System #ai #architecture #career #llm Introduction. What if every stage of your life mapped precisely onto one of the three LLM architectures? Here's how I lived through each one. I've spent years studying how AI systems learn, represent knowledge, and generate outputs. But it wasn't until I sat back and looked at my own life that something clicked.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
Discussion
0 commentsMore from DEV.to (Top)
