How to Never Lose Context Between AI Sessions

Source: Dev.to

If you use ChatGPT, Claude, or Gemini for real work, you’ve hit this wall:

Day 1: You spend an hour building a feature with Claude. It understands your codebase, your architecture decisions, your constraints. Everything clicks.

Day 2: You open a new session. Claude has no idea who you are.

You spend the first 15 minutes re‑explaining everything. You forget to mention that edge case you discussed yesterday. Claude makes a suggestion you already rejected. You correct it. Repeat.

This is the AI context problem, and it gets worse the more you use AI.

Why built‑in memory doesn’t solve it

ChatGPT has memory. Claude has project files. Gemini has conversations. But none of them actually solve the handoff problem.

• ChatGPT memory stores preferences (“I like Python”, “use tabs not spaces”). It doesn’t remember that yesterday you decided to use PostgreSQL instead of MongoDB because of the write‑heavy workload, or that there are three unfinished TODOs from your last session.
• Claude’s project knowledge is better—you can attach files—but you must manually maintain those files, and it doesn’t work across tools. If you switch between Claude and ChatGPT (which many of us do), you’re maintaining two separate contexts.
• Gemini’s conversation history is just that—history. Scrolling through 200 messages to find “what did we decide about the API schema?” isn’t context management; it’s archaeology.

The real problem: sessions are disposable

The root issue is structural. AI conversations are designed as disposable interactions, but real projects aren’t disposable. They span days, weeks, months and accumulate decisions, trade‑offs, and institutional knowledge.

Every time you start a new AI session, you’re essentially onboarding a new team member who has amnesia—and you do it multiple times per day.

What actually works: structured handoffs

After burning too many hours on re‑explaining, I started writing handoff notes at the end of each session. A quick summary of:

• What we were working on
• What decisions were made (and why)
• What’s left to do
• What the AI needs to know to continue

I’d paste this at the start of my next session. The difference was night and day. Claude would pick up mid‑thought, reference yesterday’s decisions correctly, and avoid suggesting things we’d already tried.

The problem? Writing these notes manually takes 5–10 minutes, and I’m lazy. So I’d skip it and then spend 15 minutes re‑explaining anyway.

Automating the handoff

This is why I built Lore. The idea is simple:

1. When you finish an AI session, paste the conversation into Lore.
2. It extracts a structured handoff — status, key decisions, TODOs, blockers.
3. In the next session, paste the handoff. Your AI resumes instantly.

The handoff isn’t just a summary; it’s structured data:

• Session status — completed, in progress, blocked
• Key decisions — only things you explicitly committed to (not suggestions)
• TODOs — with priorities and deadlines extracted from the conversation
• Blockers — what’s preventing progress
• Resume checklist — exactly what the next session needs to start with

Structured text is parsed much better by AI models than free‑form notes.

Cross‑tool context

I use Claude for coding, ChatGPT for brainstorming, and Gemini for quick research. None of them talk to each other.

Lore works as a bridge. The handoff is plain text—paste it into any AI, any platform. Your project context lives outside any single tool.

Over time, Lore builds a Project Summary—an evolving snapshot of your project’s goals, decisions, and progress. This becomes your project’s institutional memory, independent of which AI you’re using on any given day.

Practical tips (even without Lore)

• End every session with a summary. Ask your AI: “Summarize what we decided, what’s done, and what’s left.” Save this somewhere.
• Start every session with context. Paste your summary before your first question. Example: “Here’s where we left off: [summary]. Let’s continue with [next task].”
• Keep a decisions log. Record why you made a decision: “We chose X over Y because of Z.” This prevents re‑relitigating the same debate.
• Separate project context from session context. Project facts (architecture, constraints, goals) are long‑lived; current task, blockers are short‑lived. Update them differently.
• Use structured formats. Bullet points and headers, not long paragraphs. AI models respond much better to structured input.

The bigger picture

AI tools are getting better at memory. ChatGPT’s memory is improving. Claude Code has CLAUDE.md files. But the fundamental problem remains: your projects live across multiple tools, multiple sessions, and multiple days.

Until AI tools solve cross‑session, cross‑platform context natively (which may take years), the handoff layer is the missing piece.

Whether you use Lore, manual notes, or your own system—invest in your handoff workflow. The two minutes you spend capturing context saves fifteen minutes of re‑explaining. Every single session.

Lore is free to use (20 conversions/day, no signup). If you try it, I’d love to hear your feedback.