The Myth of AI Memory (And How to Build Something Better)

The biggest misconception in AI, and leveraging that knowledge to scale your AI strategy

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One of the most common misconceptions about AI tools is that they're constantly learning from your conversations, getting smarter with each interaction. The reality? They're not training or learning from your chats at all. While tools like ChatGPT and Gemini have started adding basic 'memory' features, these aren't making the AI smarter—they're more like digital sticky notes that the AI creates about you while you chat.

Just as finding random sticky notes from different projects scattered across your desk rarely helps (and often confuses things), having AI randomly remember details from past conversations can make its responses less reliable, not more. It's not learning or getting smarter—it's more like an enthusiastic but disorganised assistant who jots down random bits of information about you and tries to use them in every future conversation, whether they're relevant or not.

But here's the good news—you can build something much better. Instead of relying on these scattered digital sticky notes, you can create controlled, intentional context that works for your whole team. This isn't just about getting better results; it's about building reliable, scalable workflows that deliver consistent value across your organisation.

When Memory Features Get in the Way

Picture this: You're racing to finish an important client proposal. The AI keeps suggesting casual language because it "remembers" you once mentioned liking conversational writing in a social media brainstorm. Or worse, it starts referencing details from a completely different client's project because those were stored in its memory.

It's like having an assistant who can't tell the difference between your personal notes and professional documents, mixing them all together at the worst possible moments. This isn't just annoying—it can make your team's work inconsistent and potentially expose sensitive information where it doesn't belong.

Why Context Matters More Than Memory

The quality of AI responses comes down to one thing: context. Think of it like briefing a new team member—you wouldn't want them randomly remembering bits from different conversations. Instead, you'd give them clear, relevant information for the task at hand.

Companies like OpenAI and Google are experimenting with memory features as a shortcut to maintaining context, but for businesses, this creates more problems than it solves. That casual tone you used in a brainstorming session? It might show up in your formal client proposal. The specific requirements from one project? They might leak into another where they don't belong.

Building Better Systems for Context

Instead of letting AI tools randomly remember things about you and your work, here's how to build a system that actually works:

1. Create Controlled Context

• Develop clear, comprehensive documents about your business and projects
• Store your company's key information, tone, and preferences in dedicated spaces
• Use features like Custom GPTs and Claude Projects to create reliable, purpose-built tools for specific tasks and workflows

2. Make It Work for Teams

• Build prompts that deliver consistent results regardless of who uses them
• Create shared knowledge bases that everyone can access
• Document what context should be included for different types of tasks
• Turn off automatic memory features when consistency matters

3. Maintain Control and Visibility

• Know exactly what information is influencing your AI outputs
• Update and improve your shared context based on real results
• Keep sensitive information properly segregated

Real-World Implementation

Let's say you're using AI to help write proposals. Instead of hoping the AI remembers the right things about your business, you might:

1. Create a Custom GPT or Claude Project that includes:

• Your company's core value propositions
• Standard pricing structures
• Preferred formatting and tone
• Common objection handlers
• Case studies and proof points

2. Build clear processes for:

• What client information to include in prompts
• How to structure requests for different types of proposals
• When to use fresh conversations versus ongoing projects

The result? Anyone on your team can generate consistently excellent proposals, with complete control over what context influences the output. Check in for next week’s newsletter on how to build Custom GPTs and Claude Projects for your workflows!

Making It Scale

The beauty of this approach is how well it scales. When you need to:

• Update pricing? Change it in one place
• Add new service offerings? Update your shared context
• Bring on new team members? They can tap into the same tools and get the same quality results

This isn't just about avoiding the unpredictability of random memory features—it's about building systems that make your entire team more effective.

This Week's Prompt

Try this simple experiment to see the power of intentional context:

Create a one-page document about your business that includes:

• Your company's main services or products
• Your target customers
• Your brand voice and values
• Key differentiators from competitors

Tip: Use Perplexity.ai to write the initial document with the prompt: Go to this URL and write a document about my business, it should include the following information, if the details aren’t obvious infer them:

• My company's main services or products
• My target customers (you will likely need to edit this)
• Your brand voice and values
• Key differentiators from competitors

Now, try using this document in two ways:

1. Share it with your AI tool at the start of a conversation about your business (sales strategy brainstorm, writing a proposal, or working through a particular challenge)
2. Ask for the same output without sharing the document

Compare the results—you'll see how providing clear, intentional context creates more accurate, reliable outputs than hoping the AI picks up the right details on its own.

This Week's Top AI News

1. New research showcases AI's ability to simulate human responses with remarkable accuracy. The study used AI to interview humans, then analysed the transcripts to generate responses to questions—and the results were surprisingly close to actual human answers. While it might not replace traditional research methods, imagine being able to test hypotheses and gather insights at scale. The implications for customer research and understanding are huge!
2. ChatGPT 4o gets another update, and this time it's showing promise. After some initial disappointment with recent releases, my early testing suggests this version might actually be an improvement. Now's the time to revisit your workflows—you might find they're working better than before, or need a slight adjustment.
3. Anthropic secures a massive $4 billion investment from Amazon. As the makers of Claude.AI (still my favourite model most of the time), Anthropic has been shipping improvements at an impressive pace. They're emerging as the only real competitor to ChatGPT, and with this level of backing, the AI race is getting very interesting. I'm excited to see where they take things next.

Transform How Your Business Uses AI

At Erictron AI, we don't just teach theory—we help you build intentional, controlled AI systems that deliver immediate value. Our workshops are carefully designed around your business context, helping teams move beyond random experimentation to create reliable, scalable AI workflows.

What makes our workshops different?

• We start with your business objectives and context
• Build practical solutions during the session that you can use immediately
• Create systems for consistent, reliable outputs across your team
• Focus on measurable ROI and immediate implementation
• Leave you with documented processes that scale

Whether you're just starting with AI or looking to scale your existing implementation, our workshops help you build the right foundation for success. No more hoping AI picks up the right context—instead, create intentional systems that work for your whole organisation.

Ready to move beyond trial and error? Book a free discovery call and let's discuss how to build AI workflows that actually work for your business.