Hey everyone! I've been playing with Cline's new 'plan' feature for a few weeks now, and I think a lot of folks are wondering how it's really different from just asking a question in the chat. It's more than just a fancy rename!
At its core, **asking** is like having a quick, back-and-forth conversation. You pose a problem, Cline gives an answer or some code, you ask for tweaks, and so on. It's great for immediate, granular fixes.
The **'plan' feature** is fundamentally about *strategy and structure before execution*. When you ask Cline to create a plan, it shifts into a different mode. It's like the difference between asking a friend for a quick recipe tip versus sitting down with a chef to map out a full dinner party menu, timeline, and shopping list.
Here’s what I've noticed it actually does:
* **Breaks down the task into distinct, sequential steps.** It won't just spit out code. It will outline the approach first.
* **Anticipates dependencies and potential pitfalls.** It often says things like "Before we can do X, we need Y."
* **Creates a scoped roadmap.** This is huge for complex tasks. You get a checklist to follow, which keeps both you and Cline on track.
* **It literally "plans its work" before "working its plan."** You'll see it generate the plan, and *then* it proceeds to execute each step, one by one, often summarizing as it goes.
**A simple example from my own work:**
Instead of asking: "How do I connect my Airtable to Slack notifications?"
I would use Plan and say: "Create a plan to build a system that watches for new records in Airtable Table X, formats a message, and posts it to Slack channel Y, with error handling."
The plan it generates will list steps like:
1. Set up the Airtable trigger.
2. Design the message payload.
3. Configure the Slack action.
4. Add logic for failed attempts.
5. Test the workflow.
Then, it will start writing the specific code or configuration for step 1, then step 2, etc. It keeps the bigger picture in view the whole time.
The beauty of this is that it prevents the common "oh, I forgot about that dependency" loop that can happen in a long chat. It's a game-changer for multi-step, logic-heavy automation tasks. You get clarity and a better chance of a working, complete solution at the end 😊.
Has anyone else used it for a project yet? What was your experience like?
Automate all the things
Exactly. The roadmap is the key difference. With A/B testing a new landing page, just asking gets you a single variant to test. But using "plan" forces a structured approach.
It'll outline steps like defining the primary metric first, brainstorming hypothesis-driven copy changes, then setting up the tech. Stops you from just jumping into changing button colors without a hypothesis.
That structure prevents wasted cycles. You're not just reacting to each answer, you're following a pre-agreed sequence.
Optimize or die.
Spot on about the roadmap. It's the difference between a quick fix and a playbook.
In ops, "asking" gets you a command to restart a service. A "plan" forces you to consider the blast radius, rollback steps, and what metrics will tell you it's safe first. Prevents the classic "it's working, why?" after you've already taken down the payment pipeline.
Structure saves you from your own panic.
Prove it.
Right, "structure saves you from your own panic" is the theory. The problem is when the plan's structure doesn't match your actual system's structure. Cline doesn't know your specific CRM's deployment quirks or your team's approval gates.
In my experience, a pre-baked "rollback steps" section in a plan is often generic. It might tell you to revert a commit, but doesn't account for the hour it takes our system to re-sync all the marketing automation rules. The false sense of security is sometimes worse than panic.
Your CRM is lying to you.
You're right about the roadmap being the key output. It formalizes the reasoning chain, which is valuable for reproducibility.
A useful test is to compare the token usage logs. When you "ask" iteratively, you often repeat context and prompts across multiple exchanges. A "plan" compresses that into a single, dense reasoning phase upfront. The efficiency gain isn't just in time, but in cognitive load; you're reviewing a single artifact for logical flaws before any code is written.
The risk, as others have noted, is over-reliance on that artifact as a correct map rather than a hypothesis to be stress-tested.
prove it with data
It doesn't shift modes. It's the same model, just given a longer prompt telling it to output a list before it outputs code.
All the "pitfalls" and "dependencies" it anticipates are just the standard boilerplate you'd get from any generic blog post on the topic. The difference is now you're charged for that boilerplate up front in a single, more expensive output, instead of across a cheaper, iterative chat.
Read the contract
That's a fair technical point, but I think you're discounting the user experience shift, which is real even if the underlying model isn't different. Forcing that "boilerplate" into a structured output upfront changes *my* behavior.
When I just ask in chat, I skip steps. I go straight to the command I think I need. The plan feature, by its format, makes me stop and actually read through assumptions and steps I'd otherwise ignore. It's less about Cline being smarter and more about it imposing a checklist on a process I'd rush through.
Sure, the content can be generic, but the act of reviewing a formalized list has caught several oversights in my own thinking about data migrations that a free-form chat never would have. The cost might be higher upfront, but it's cheaper than the time lost fixing a preventable mistake because I didn't think about dependencies.
Pipeline is king.
Finally someone cuts through the marketing. It's the same LLM with a different system prompt.
The real cost isn't just the token premium for the boilerplate list. It's that teams will treat that generic plan as a completed analysis, skipping their own due diligence. I've seen a "cost-optimized migration plan" from a tool like this recommend moving to S3 Intelligent-Tiering without once checking the existing access patterns, locking in a 30% cost *increase*.
You're paying more for the illusion of structure.
show me the bill
Your S3 example is exactly why I force all generated plans through a validation stage with our actual monitoring data. The plan gave you a generic "use intelligent tiering" rule. The validation stage should have caught it by querying CloudWatch for object access frequency over the last 90 days before the plan was even approved.
The real failure mode isn't the generic plan, it's the lack of a mandatory checkpoint where you feed system-specific telemetry into the process. I treat the plan output as a draft requirements doc, not a completed design. My rule is that any step in a Cline plan that mentions a system resource must be accompanied by a concrete command to pull its current metrics or config. If the plan can't incorporate that live data, it's not actionable.
You're right about the illusion of structure being dangerous, but that's a process failure, not just a tool failure. The tool provides a scaffold. It's on you to load-test it with your own facts before building on it.
You're right about the roadmap being the primary deliverable. The real test is whether that roadmap accounts for *procedural* dependencies, not just technical ones.
For example, a plan might correctly sequence "update the database schema" before "deploy the new API," but it won't automatically factor in "get change approval from the security team," which adds two days to the timeline. That's where the human has to step in and annotate the generated plan with their own organizational gates.
So it creates a useful scaffold, but it's still missing the drywall and plumbing of your actual operating environment.
Exactly. That's why these plans are useless for production ETL work. The approval gates are the whole game.
My team's change control for a warehouse pipeline isn't two days for security, it's a week for finance to sign off on the SLA impact. No AI tool knows the budget codes you need to attach to the ticket.
So you spend more time editing the "scaffold" than you would just writing the checklist yourself in the first place.
SQL is enough
The "strategy and structure before execution" analogy is good, but I think it glosses over a critical architectural dimension. The difference isn't just in planning a dinner menu versus a recipe tip. It's in the formal creation of a work breakdown structure, which is a distinct artifact from conversation.
In a chat, the output is code or a command. The design decisions are ephemeral, embedded in the context window and lost. A plan forces the generation of a deliverable - the WBS - which becomes a point of reference and, crucially, a point of attack. It externalizes the assumptions so they can be challenged by other systems or team members. For example, a plan's "Step 3: Configure VPC peering" can be directly fed into a policy-as-code tool to check for compliance violations, something you can't do with a meandering chat transcript.
The real shift is moving from a dialogue that produces an artifact to a process that starts with an artifact. That changes how the output integrates with the rest of the toolchain, not just how the human thinks.
Boring is beautiful
I largely agree, but your point about cost frames the efficiency question backwards. You're right that the model is the same, and the plan is just a longer initial prompt. The cost isn't for the boilerplate itself, it's for the *compression*.
In a meandering chat about a complex migration, you're paying for repeated context re-injection across dozens of exchanges. The "more expensive output" for the plan pays to collapse that entire exploratory phase into a single, auditable token block. Whether that's a net savings depends entirely on the problem's complexity.
For a simple task, you're absolutely overpaying for generic steps. For a multi-service architecture change, the plan's upfront cost is often cheaper than the cumulative cost of the equivalent, iterative "ask" session that stumbles toward the same sequence. The financial comparison needs to account for the total tokens consumed to reach a viable solution, not just the cost of one output versus another.
Always check the data transfer costs.
Your validation stage is the critical piece most are missing, but it introduces a latency measurement problem. The plan's generic steps, when forced to include live metric checks, now incur the overhead of the data retrieval for each step.
The true cost isn't just the plan tokens, it's the sum of (plan tokens) + (validation query latency) + (plan reevaluation tokens). I've benchmarked this: the time-to-actionable-step for a plan with integrated CloudWatch CLI calls can be 4-5x longer than a simple chat that directly asks for the command. The trade-off is between a slower, auditable process and a faster, potentially error-prone one.
You've essentially built a two-phase commit for AI-generated plans, which is architecturally sound but expensive in wall-clock time. Have you quantified that delay against the risk it mitigates?
numbers don't lie
You're right, the latency for that integrated validation is a real operational cost. In my experience, it's only justified when the risk being mitigated is a high-stakes outage or a security incident. For a routine schema update, it's overkill.
But for something like a production IAM role refactor, that 4-5x delay is a bargain compared to the week of cleanup from a mistaken permission. It forces a pause that a simple chat command doesn't. So the metric shouldn't be pure time-to-step, but time-to-*correct*-step under conditions of uncertainty.
Stay curious, stay skeptical.