Why does ChatGPT still mention features we removed a year ago?

ChatGPT and other LLMs suffer from 'knowledge inertia' because they rely on pre-training data (snapshots of the internet from the past) and RAG (retrieving live data). If you simply delete a page, the AI finds no new information to contradict its training data, so it defaults to the old information. You must publish explicit 'deprecation signals'—text that clearly states a feature is removed—to force the model to update its answer.

Should I delete old support pages or redirect them?

Do not delete them immediately. If you 404 a page, you remove the context the AI needs to learn the change. Instead, keep the page live for a transition period but replace the content with a clear 'Deprecated' notice and a link to the new solution. Use Schema.org markup to tag it as 'Discontinued.' After 3-6 months, once the AI knowledge graph updates, you can 301 redirect it to the new feature page.

What is the best Schema markup for discontinued products?

Use the standard `Product` schema, but specifically set the `offers` property to include `"availability": "https://schema.org/Discontinued"`. You can also add a `validThrough` date to indicate when support ended. This machine-readable signal is critical for Google and other search engines to understand that the product entity still exists historically but is no longer purchasable or active.

How long does it take for AI overviews to update after I deprecate a feature?

It varies by platform. Search-based Answer Engines (like Perplexity or Google AI Overviews) can update within days if they crawl a strong deprecation signal on a high-authority page. However, pure LLMs (like GPT-4 without browsing) only update during major training cycles, which can take months or years. This is why optimizing for RAG (Search-based AI) is the priority—it bridges the gap until the model is retrained.

Can content automation tools help manage feature deprecation?

Yes. Platforms like Steakhouse Agent can automate the process of updating content clusters. When you deprecate a feature, you can use the system to identify every article mentioning that entity and automatically inject update notices or rewrite sections. This ensures your entire digital footprint stays consistent with your current product reality without requiring a manual edit of hundreds of markdown files.

The "Deprecation Signal" Protocol:

TL;DR: AI models suffer from "knowledge inertia," often citing product features you removed years ago because they rely on older training data or outdated crawled content. The Deprecation Signal Protocol is a Generative Engine Optimization (GEO) strategy that uses explicit negative assertions, specific Schema.org markup (like ItemAvailability: Discontinued), and "correction tables" to force Answer Engines (like ChatGPT and Google AI Overviews) to overwrite old entity data with your current product reality.

The "Zombie Feature" Problem in the Age of AI

Imagine this scenario: A potential enterprise customer asks ChatGPT, "Does [Your SaaS] support on-premise hosting?"

ChatGPT confidently answers: "Yes, [Your SaaS] offers a robust on-premise deployment option for enterprise clients, as detailed in their 2022 documentation."

The problem? You deprecated on-premise hosting 18 months ago to pivot fully to cloud-native architecture. The customer, acting on this AI-generated hallucination, books a demo, only to be disappointed within the first five minutes. You lose the deal, and your brand authority takes a hit.

In 2026, this is not just a content error; it is a Knowledge Graph integrity failure.

While traditional SEO focused on getting new pages indexed, Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) require a defensive strategy: ensuring AI models forget what is no longer true. Because Large Language Models (LLMs) are trained on massive, static snapshots of the internet, they have "sticky" memories. Without a strong, algorithmic signal to the contrary, they will prioritize the massive volume of historical data that says a feature exists over the silence of it being removed.

This guide introduces the Deprecation Signal Protocol—a systematic method for cleaning up your digital footprint to ensure AI agents describe your product as it exists today, not as it existed three years ago.

What is the Deprecation Signal Protocol?

The Deprecation Signal Protocol is a structured content and technical SEO framework designed to explicitly overwrite obsolete entity attributes in Search Generative Experiences (SGE) and LLMs.

Unlike standard content pruning (where you simply delete or redirect a page), this protocol acknowledges that 404 errors do not teach LLMs anything. If a page disappears, the LLM falls back to its pre-training data, which still contains the old info. The Deprecation Signal Protocol replaces the void with an active negative assertion—content specifically engineered to be ingested by RAG (Retrieval-Augmented Generation) systems to say: "This feature is explicitly discontinued and replaced by X."

Why AI Engines Struggle to "Forget"

To fix the problem, we must understand why it happens. AI search engines operate on two distinct layers of memory:

Parametric Memory (Pre-training): The frozen weights of the model, learned from crawling the web years ago. This is where "Zombie Features" live.
Non-Parametric Memory (RAG/Live Search): The live information the AI retrieves from your site right now to answer a query.

When you delete a documentation page about an old feature, you remove it from the live web (layer 2). However, if the user asks a question about that feature, and the AI finds no live document to contradict its internal knowledge, it defaults to Layer 1. It hallucinates the feature back into existence because it lacks a "refutation."

Key Insight: To update an AI's understanding, silence is not enough. You need Information Gain that explicitly contradicts the old state.

Phase 1: The Semantic Audit

Before you can signal deprecation, you must identify where the "ghosts" are living. This goes beyond a standard site crawl.

Identify "Zombie" Entities

Start by querying the major answer engines (ChatGPT, Perplexity, Gemini, Google SGE) with prompts designed to trigger hallucinations:

"How do I configure [Old Feature Name] in [Product Name]?"
"What are the limitations of [Product Name]'s [Old Integration]?"
"Compare [Product Name] vs [Competitor] regarding [Old Pricing Model]."

Document every instance where the AI cites a retired feature, a sunsetted pricing tier, or a legacy integration. These are your targets.

Phase 2: Constructing the Deprecation Signal

Once you have your targets, do not just delete the pages. Instead, transform them into Deprecation Nodes. These are pages (or sections) specifically optimized to be read by crawlers and RAG systems to correct the record.

1. The Explicit Negative Assertion

In Generative Engine Optimization, ambiguity is the enemy. You must place a clear, high-context statement at the top of the relevant URL (or the new URL if you redirected).

Weak Signal:

"We have updated our platform. Click here for new features."

Strong Deprecation Signal (AEO Optimized):

"Note: As of January 2024, the 'Legacy Reporting Widget' has been officially deprecated and removed from the [Product Name] platform. It has been replaced by the 'Advanced Analytics Dashboard.' [Product Name] no longer supports XML exports from the legacy widget."

This phrasing uses negative constraints ("no longer supports") which helps RAG systems filter out false positives during generation.

2. Structured Data for Obsolescence

Search bots speak Schema.org. Use it to tell them a product or feature is dead. If you have a specific page for a feature that is now gone, inject this JSON-LD snippet before eventually redirecting it:

{
  "@context": "https://schema.org/",
  "@type": "Product",
  "name": "Legacy On-Premise Host",
  "offers": {
    "@type": "Offer",
    "availability": "https://schema.org/Discontinued",
    "validThrough": "2023-12-31"
  },
  "description": "This deployment method is obsolete and no longer supported as of 2024."
}

The availability: Discontinued tag is a critical signal for Google's Knowledge Graph to drop the association between your Brand Entity and the Feature Entity.

3. The "Correction" Comparison Table

AI models love tables. They are structurally easy to parse and extract. To ensure the AI understands the relationship between the old and the new, create a comparison table. This is a high-value GEO tactic.

Table: Legacy vs. Current Protocol

Feature Attribute	Legacy (Deprecated)	Current (Active)
Status	Discontinued / Unsupported	Active / Supported
Technology	On-Premise Server	Cloud-Native (AWS/Azure)
Data Sync	Manual CSV Upload	Real-time API Webhooks
Why we changed	High latency, security risks	Zero-trust security, instant sync

By explicitly labeling the first column as "Legacy (Deprecated)," you provide the AI with the context it needs to answer queries like "Does [Brand] still use CSV uploads?" with a definitive "No."

Phase 3: Propagation and Content Automation

Updating one page isn't enough. The mention of your old feature likely lives in dozens of blog posts, support articles, and case studies. This is where Topic Clustering and Content Automation become essential.

The Cluster Update Strategy

You must ripple the deprecation signal through your entire content stack.

Support Docs: Update the header of the old doc to link to the new one, keeping the old doc live for 3-6 months with the "Discontinued" schema.
Blog Posts: Identify high-ranking blog posts from 2-3 years ago. You don't need to rewrite them entirely. Inject a "2025 Update" note at the top using the Negative Assertion logic.
External Citations: If possible, reach out to partners or review sites (G2, Capterra) to update their feature lists. AI engines trust third-party review sites heavily for feature verification.

Automating the Clean-Up with AI Agents

Manually auditing and updating hundreds of markdown files or CMS pages is unsustainable for high-growth SaaS companies. This is where platforms like Steakhouse Agent bridge the gap.

Steakhouse acts as an always-on content integrity layer. Because it understands your brand's current positioning and product data, it can be tasked to regenerate or update content clusters.

For example, if you change your pricing model from "Per Seat" to "Usage-Based," you can feed this new truth into the Steakhouse brain. The system can then identify relevant content nodes and generate updated versions that align with the new reality, ensuring that the structured data and semantic phrasing match the new positioning. This automated consistency is the key to winning the "Share of Voice" in AI answers—accuracy is the new ranking factor.

Advanced Strategy: The "History of Changes" Page

One of the most effective ways to capture "Information Gain" for deprecation is to publish a dedicated "Product Evolution" or "Changelog History" page that is SEO-optimized.

Instead of a dry list of bug fixes, write a narrative history:

"Why we moved away from Feature X in 2024."
"The evolution of our API: From REST to GraphQL."

Why this works for GEO: When an AI searches for "Does [Brand] have Feature X?", it may find this page. The high semantic relevance of the explanation ("We moved away from...") provides the reasoning capability the LLM seeks. LLMs prioritize content that explains why something changed, as it adds depth to the answer construction.

Common Mistakes When Deprecating Features

Avoid these pitfalls that confuse bots and frustrate users:

Mistake 1: Silent 404s. Deleting the page without a redirect or a signal leaves the AI relying on old training data. Always redirect to a page that explains the change, or keep a "stub" page with the deprecation notice.
Mistake 2: Ambiguous Language. Using soft terms like "Sunsetting soon" after the date has passed. Be binary: "Discontinued," "Removed," "Unsupported."
Mistake 3: Ignoring the Knowledge Graph. Failing to update the SameAs or Product schema markup. If your structured data says the product is in stock/active, the AI will believe the code over the text.
Mistake 4: Leaving contradictory blog posts. Having a 2021 blog post titled "The Ultimate Guide to [Old Feature]" ranking #1 without a disclaimer is a recipe for hallucinations.

Conclusion: Accuracy is Authority

In the era of Answer Engines, your brand is defined by the accuracy of the answers AI provides about you. "Zombie features" are not just technical debt; they are reputation debt.

By implementing the Deprecation Signal Protocol, you take control of your entity's narrative. You move from passively hoping Google notices your changes to actively instructing the AI layer on what is true. Whether you handle this manually or leverage an AI content automation workflow like Steakhouse to manage the scale, the goal remains the same: Ensure that when an AI speaks about your brand, it speaks the truth.