May 31, 2026

How to Maintain AI Conversation Context Across Multiple LLMs

Fewer and fewer people use only one AI.

You might use ChatGPT to quickly organize ideas, Claude to deeply review complex structures, Gemini or Perplexity to find the latest materials or compare multiple sources, and tools like Grok, Copilot, DeepSeek, or Mistral when you need different perspectives.

Each LLM has different strengths.

Rather than entrusting all tasks to one AI, users choose the most suitable AI depending on the situation.

The problem is that while the user's thinking continues as a single flow, AI conversations are separated by service.

Thinking Continues, but Chat Windows Are Not Connected

Consider a situation where you advance a single project across multiple AIs.

Define the problem and set the direction in ChatGPT.
Deeply review the structure and exception conditions in Claude.
Find related materials and different perspectives in Gemini.
Confirm evidence and sources in Perplexity.
Proceed with actual implementation in Copilot or other tools.

From the user's perspective, it is all the same task.

But each AI exists in a different chat window and a different service. Claude does not know the criteria decided in ChatGPT, and Gemini does not inherit the exception conditions organized in Claude.

Every time you switch AI, you have to re-explain the previous situation.

Copying and Pasting Alone Is Not Enough

The simplest method is to copy a previous conversation and paste it into a new AI.

In short conversations, this approach can work. But as work grows longer, problems arise.

You have to re-judge which parts to copy.
Important decisions and temporary ideas mix within long raw text.
Already-discarded options may be conveyed to the new AI.
It is unclear what criteria the user ultimately adopted.
Long conversations unnecessarily consume the new AI's input limit.
You repeat the same organizing work every time you start a new conversation.

Transferring all raw text does not mean the flow of thought is accurately conveyed.

What is needed is not the entire previous conversation but context about what problem was addressed so far, what was decided, and where to continue from.

Even Summaries Can Lose Important Context

Another approach is to summarize a long conversation briefly and convey that.

But simple summaries easily miss the following information.

Why this problem was started
What options were compared
What conditions led to excluding a specific direction
What the criteria for the final judgment were
What problems remain unsolved
Where to immediately continue in the next conversation

Conveying only the conclusion means the new AI can know the result but has difficulty understanding the process that created the judgment.

As a result, it may re-propose options already reviewed, or produce answers that undermine criteria the user has finalized.

The More LLMs You Use, the More You Become a Context Manager

Because AI services are separated, users must manage context themselves.

Every time the user starts a new conversation, they repeat the following.

Explain the currently ongoing task.
Re-organize what was previously decided.
Inform about excluded directions and their reasons.
Convey currently remaining problems.
Verify the new AI has properly understood the context.

The purpose of switching AI is to get better help, but in practice, much time is spent connecting context between AIs.

The ability to not lose thoughts across multiple AIs is becoming more important than the ability to use multiple AIs.

What 5BY Connects Is Not Accounts but the Flow of Thought

5BY is not a tool that merges multiple AI services into one screen.

Each AI is used as-is in its existing service and chat window. Users can freely choose the AI suitable for their task.

What 5BY aims to connect is not the AI services themselves but the user's thoughts formed within them.

For this, Anchor and Handoff play important roles.

Anchor Is a Reference Point for Preserving the Flow So Far

Anchor is a feature where the user personally leaves the important context formed in a conversation as a reference point.

An Anchor should contain not just the last answer but the flow needed in the next conversation.

The problem currently being solved
Facts confirmed so far
The direction the user chose
Criteria and conditions to uphold
Excluded options and reasons
Unresolved problems
The point to continue from

Instead of copying the entire conversation, the user can leave a coordinate for continuing their thinking.

Handoff Connects That Reference Point to a New Conversation in Another AI

Handoff is the process of making the context left in an Anchor usable in a new conversation.

For example, after organizing the project's direction and criteria in ChatGPT, you can continue deeper review in Claude.

Instead of re-explaining everything from scratch to the new AI, you provide the context finalized in the previous conversation as the starting point.

The purpose of Handoff is not to replicate answers as-is.

It is to help the new AI start work with an understanding of the following.

What the user is trying to do
What judgments have been made so far
What must not be changed
Which problem to continue addressing first

Even When AI Changes, the User's Criteria Must Be Maintained

The most dangerous thing in using multiple AIs is not that answer expressions differ.

It is that criteria the user has already set disappear each time the conversation changes.

In one AI, you may have decided to prioritize simplicity, but another AI might propose a complex structure. In a previous conversation, you may have decided to exclude a specific feature, but in a new conversation, that feature may reappear as a core proposal.

The new AI is not wrong; the problem arises because it was not conveyed the previous judgment.

For Handoff to work properly, the user's decisions and criteria to preserve must be conveyed clearly, beyond a simple topic summary.

You Should Be Able to Use Each AI's Strengths While Continuing the Same Work

The purpose of using multiple AIs is not to completely replace one AI with another.

It is to leverage each AI's strengths at the moments they are needed.

When ideas are needed, use an AI strong in ideation.
When complex logic is needed, use an AI strong in analysis.
When latest evidence is needed, use an AI strong in search.
When implementation is needed, use an AI suited for code work.

Tools may change, but the user's project and thinking must continue.

5BY aims to connect the reference point created in a previous conversation to the starting point of a new conversation, so the user does not go back to the beginning every time they switch AI.

What Must Be Connected Is Not Conversation Raw Text but the Context of Judgment

Conveying all conversation content as-is is not efficient.

What matters is the context the next AI needs to continue the work.

Which problem is important
What has already been decided
What constraints must be upheld
What is not yet finalized
Where the next task should begin

When this information is maintained, the user can continue developing the same thinking even when AI changes.

Conversely, when this context disappears, no matter how much conversation raw text remains, you start over from scratch with the new AI.

The Foundation Needed in an Era of Using Multiple AIs

Going forward, AI models will become more diverse and each one's strengths will become clearer.

Users are likely to move across multiple AIs rather than staying with one.

What will be needed then is not unifying all AIs into one.

It is ensuring that the user's thoughts and decisions do not break even when AI changes.

5BY's Handoff aims to help you restart from the important context of a previous conversation, regardless of whether you use ChatGPT, Claude, Gemini, or another AI.

AI may change.

Chat windows may change.

But the flow of thought the user has built should not go back to the beginning every time.