<!--
metadata:
  title: "AI agent and LLM optimization"
  platform: "linkedin"
  objective: "Recommendations for making a LinkedIn profile easy for external AI tools, parsers, and enrichment workflows to understand."
  status: "draft"
  last_updated: "2026-04-24"
  tags: ["linkedin", "ai-agents", "llm", "parsing"]
  agent_priority: "high"
-->

# AI agent and LLM optimization

> This file defines the rules for optimizing a LinkedIn profile so external AI tools can read it robustly, even when their exact data pipelines are not public.

---

## 1. Overview

External recruitment tools do not all work the same way. Some rely on browser automation, some on exported or enriched profile data, and some on scraped text. The details are rarely public. The practical response is simple: write a profile that remains understandable after plain-text extraction.

## 2. Vector search readiness

**Rule:** Use standard industry terminology. If the profile uses overly creative titles or uncommon names for common tools, external matching systems have less reliable text to work with. Standard role names and standard skill names are safer.

**Rule:** Explicitly state the "Ideal Role" and core competencies. Many AI-driven workflows compare profile text with a job description. If the About section explicitly names target roles and core competencies in clear language, the match is easier to justify.

## 3. Structured data completeness

**Rule:** Fully populate all structured data fields. Do not rely entirely on the About section. Complete the Skills, Certifications, Education, Experience, and Featured sections so important facts are duplicated in the places parsers most often inspect first.

**Rule:** Add proof links where the claim is strongest. When a project, paper, repository, talk, or credential is public, link it in the most relevant section. Plain claims are weaker than claims with a visible proof source.

## 4. Examples

Good example:
```text
<!-- CORRECT: Explicit terminology for an external parser -->
Targeting Senior Backend Engineer roles. Core competencies include:
- Distributed Systems (Go, gRPC)
- Cloud Infrastructure (Kubernetes, AWS)
- Database Optimization (PostgreSQL)
```

Bad example:
```text
<!-- WRONG: Vague terminology that gives matching tools little usable signal -->
I am a passionate tech enthusiast looking for my next big adventure in the cloud space. I love building things that scale and working with data.
```

---

*Next step: Configure your foundational settings in [Profile architecture](./profile-architecture.md).*
