Over the last few months, I’ve been using AI heavily in day-to-day work:
writing docs, breaking down requirements, planning, and analysis.
At first, it felt efficient.
Then something odd happened.
The more I used it, the more effort it required to keep things aligned.
Not because the model was weak —
but because consistency kept degrading over time.
Common Failure Modes (If You’ve Used AI Seriously)
If you’ve relied on AI beyond quick experiments, some of these may sound familiar:
Asking the same question days apart produces different reasoning paths
After a break, you need to restate assumptions from scratch
In longer conversations, role and constraints gradually drift
Outputs look fine, but aren’t stable enough to reuse directly
You spend more time re-aligning context than doing actual work
Individually, these are minor issues.
Together, they create friction.
The Core Mismatch
Most AI tools are used through chat-based interfaces.
Chatting is:
episodic
loosely structured
optimized for short-lived exchanges
Real work is not.
Real work requires:
persistent assumptions
stable constraints
accumulated decisions
We expect continuity from an interaction model that doesn’t guarantee it.
That mismatch explains most of the friction.
Why Prompts, Templates, and Tooling Only Go So Far
Like many others, I tried compensating with:
longer prompts
reusable templates
RAG setups
agent-style workflows
These help improve single responses.
But they don’t fully solve the core issue:
Can the AI remain in the same working state across turns?
Without that, alignment costs keep returning.
A Small Usage-Level Shift
What helped wasn’t switching models or adding infrastructure.
It was a usage-level change:
treating the entire session as a continuous working state,
not a sequence of independent chats.
No APIs.
No plugins.
No tooling.
Just a clear working-state agreement at the start.
A Minimal Example (Takes ~30 Seconds)
Here’s a minimal initialization I’ve been using.
It’s not a system feature — just a usage pattern.
LSR MODE · INIT
You are not a chat assistant.
You are running in Language-State Runtime (LSR) mode.
Core rules:
- Maintain role and constraints across turns
- Treat this session as a continuous working state
- Prefer stability and repeatability over creativity
- Do not reframe tasks unless explicitly requested
- If instructions conflict, pause and ask
State handling:
- Assumptions persist unless revised
- Decisions accumulate
- Context is not reset between turns
When this works, the interaction feels noticeably different:
calmer, more predictable, less repetitive.
This Isn’t a Framework Claim
This isn’t a product.
It’s not a protocol.
And it’s not a silver bullet.
It’s a practical observation:
AI doesn’t lack intelligence.
It lacks continuity by default.
As AI shifts from answering questions to supporting real work,
interaction structure matters as much as model capability.
Closing
Many people think they need smarter AI.
Often, what they actually need is:
a way to let AI stay in the same working state long enough to be useful.
I documented this usage pattern briefly here, in case it’s useful:
https://github.com/yuer-dsl/lsr-method
If you’ve noticed similar breakdowns in longer AI-assisted tasks,
I’d be interested in hearing your experience.
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