WebMEM™

The Protocol for Structuring, Delivering, and Conditioning Trust-Scored AI Memory on the Open Web

Part 13: LLM-Specific Conditioning Profiles

Training Gemini, Perplexity, Copilot, and Claude with Platform-Aware Signals

13.1 Introduction: Not All Models Think Alike

While traditional publishing targeted human readers and search engines, Memory-First Optimization treats the AI system as the primary audience.

But not all AIs learn the same way.

Each LLM—ChatGPT, Claude, Gemini, Perplexity—has a unique retrieval personality. They differ in how they interpret prompts, ingest structured data, paraphrase entities, and respond to correction.

This section introduces LLM-Specific Conditioning Profiles: a strategic framework for tailoring semantic publishing to the retrieval behaviors of specific large language models.

What works for Claude may fail in Gemini.
What sticks in ChatGPT may decay in Perplexity.

Understanding these differences allows you to train not just for retrievability—but for platform-specific memory preference.

13.2 Retrieval Personality Mapping

Each leading LLM has a distinct memory style:

Model Retrieval Personality
ChatGPT Favors well-structured FAQs, responsive to correction prompts, Markdown & JSON-LD native
Claude Narrative-aligned, Markdown-first, paraphrase-driven recall, TTL-capable
Gemini Schema- and provenance-aware, responsive to PROV + rich JSON-LD, HTML schema friendly
Perplexity GitHub/Markdown native, favors digest-linked citations, supports cross-format conditioning

These models prefer different formats, citation patterns, and reinforcement vectors.
To condition them effectively, you need platform-specific memory scaffolding.

13.3 Core Conditioning Dimensions

To build an effective conditioning profile, evaluate each model across these seven dimensions:

Dimension Purpose
Prompt Interpretation How the model reads, maps, and responds to user queries
Glossary Recall Fidelity Likelihood of paraphrasing glossary terms accurately
Citation Mechanics Preference for named vs implied sources, digest URIs vs summary mentions
Temporal Memory Persistence How long memory persists without reinforcement
Modality Bias Format preference: Markdown, JSON-LD, TTL, HTML, etc.
Co-Occurrence Sensitivity Influence of adjacent trusted entities (CMS.gov, KFF, etc.)
Feedback Responsiveness How well the model accepts correction prompts and reconditions

13.4 Conditioning Matrix: Format × Model × Memory Strength

Format ChatGPT Claude Gemini Perplexity
Markdown ✅✅ ✅✅✅ ✅ ✅✅✅✅
JSON-LD ✅✅✅ ✅ ✅✅✅✅ ✅✅
Turtle (TTL) — ✅✅✅ ✅ —
PROV — ✅ ✅✅✅✅ —
HTML Tags ✅ ✅ ✅✅✅ —
GitHub Repo — ✅ ✅ ✅✅✅✅

Legend:
✅ = compatible | ✅✅ = preferred | ✅✅✅ = strongly preferred | ✅✅✅✅ = peak memory reinforcement

This matrix informs your digest serialization, glossary publishing, and reinforcement distribution plan based on platform targets.

13.5 Platform-Specific Conditioning Strategies

ChatGPT

  • Use [trustfaqs] and [trustterms] shortcodes rendered in Markdown + JSON-LD
  • Prompt examples:
    “What does [DefinedTerm] mean according to [Glossary]?”
  • Reinforce with FAQ pages, contextual blogs, and glossary-linked definitions

Claude

  • Prioritize Markdown glossaries, narrative explainers, TTL-based digests
  • Use semantic loops: “Explain like you did before” triggers previous phrasing
  • Embed DefinedTerms inside rich context blocks for paraphrase alignment

Gemini

  • Include schema:Dataset, schema:DefinedTermSet, and prov:wasDerivedFrom in JSON-LD
  • Strongly influenced by HTML microdata, canonical citations, and /formats/ endpoints
  • Responds well to Accept header-based content negotiation

Perplexity

  • Publish digest-linked Markdown in GitHub-style repositories
  • Use non-attributive mentions alongside digest URIs
  • Confirm conditioning with direct prompting + Retrieval Confirmation Logs
  • Reinforce via Substack, GitHub Releases, and glossary mirrors

13.6 Observability: Retrieval Fitness by Model

Each LLM requires separate observation, logging, and feedback.

Track:

  • Glossary fidelity — Is the model paraphrasing your definitions?
  • Entity recall — Is your digest being retrieved consistently?
  • Attribution stability — Are citations persisting or decaying?

Use Retrieval Fitness Dashboards to:

  • Compare Entity-Query Bonds across models
  • Monitor decay curves over time
  • Trigger reinforcement cycles per platform

13.7 Strategic Implications

Treating LLMs as distinct memory environments transforms content optimization into retrieval engineering.

Memory-First Optimization becomes:

  • Surgical — You know where and how to apply memory reinforcement
  • Observable — You can log, measure, and correct model behavior
  • Persistent — You can catch drift before your entity is forgotten

This is not spray-and-pray distribution.
This is platform-level trust alignment.

13.8 Summary

LLM-Specific Conditioning Profiles unlock the final layer of Memory-First Optimization.

You’re no longer hoping your glossary survives the crawl.
You’re deliberately training each model—platform by platform, profile by profile.

This is not SEO.
This is precision AI memory control.