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LinkedIn — 3 Variations

Variation 1 — The Admission

The Admission

I hit my Claude Max weekly limit in three days. Again.

While I wait for it to reset heres what I figured out so it doesnt happen a third time.

The first thing I learned is that prompt structure matters more than prompt length.

Theres research showing language models pay more atention to the beginning and end of what you give them. The middle gets lost. It resembles how humans process information. You remember the start and the finish. Everything in the middle fades.

So I started trimming the middle out of my prompts. Moved the most important context to the top and the most important request to the bottom.

Output quality stayed the same or got better. Token usage dropped.

The second thing I learned is that giving Claude less actualy produces more focused results.

I spent three months dumping my entire project into every prompt. All the files all the context all the background. Claude got overwhelmed by the noise the same way a new employee gets overwhelmed on day one.

I built a system that does the trimming for me. A vector database with everything Ive built stored as compressed embeddings. One script retrieves only whats relevnt to the current question. 76% fewer tokens. Same or better output.

The last thing I learned is that the tool is only as efficent as the enviorment around it.

You wouldnt give a contractor a job without blueprints. You shouldnt give Claude a prompt without organized context. Building that organization is the work nobody talks about but everybody who gets consistant results has done it.

Three months into learning this. Still figuring it out. But the limit reseting in three days stopped feeling like a failure and started feeling like data.

Variation 2 — The Blunt One

The Blunt One

Three days. Thats how long my weekly Claude Max limit lasts.

Im not proud of it. But while the tokens reset heres everything I picked up about prompt enginering that I wish I knew sooner.

First thing. The beginning and end of your prompt matter more than the middle.

Theres actual research on this. Language models process prompts the same way humans process new information. The first thing you say sets the frame. The last thing you say gets the most weight. Whats in the middle gets deprioritized.

I was front loading my prompts with background and burying the actual request at the bottom. Backwards.

Flip the structure. Most important thing first. Most important ask last. Everything else is noise.

Second thing. External memory beats internal context every time.

The reason I was burning tokens so fast was I kept trying to give Claude everything upfront. The entire project. Every file. Every conversation.

You cant do that. Theres a limit to how much fits and most of it doesnt get used anyway.

I built a vector database that stores everything Ive learned and built across my projects. 1066 chunks of knowlege indexed as compressed embeddings. One script pulls only whats relevnt to the current session.

76% fewer tokens per session. No loss in output quality.

Third thing. Organize the enviorment before you write the prompt.

Claude is only as good as what you give it. If your context is messy your output will be messy. The prompt is the last step not the first.

Three months into this jouney. Still making mistakes. But theyre diferent mistakes than last month and thats excatly how you know its working.

Variation 3 — The Curiosity Gap

The Curiosity Gap

Theres a reason some people get 10x more out of the same Claude plan.

I found out what it was after burning through my weekly limit in three days. Heres what I learned while I wait for it to reset.

It comes down to one thing. External memory.

Most people treat every Claude session like a blank slate. They dump everything into the prompt hoping the model figures out whats relevant. It doesnt work. You burn tokens on context Claude doesnt even use and the actual output suffers for it.

What the people getting more out of it have is organized context that lives outside the prompt.

I built mine this week. A vector database running on my server with 1066 chunks of everything Ive built and learned accross my projects. Every time I start a session one script runs a semantic search against it and returns only whats relevnt to what Im working on right now.

Before I built it I was giving Claude my entire project every session. 4x the tokens. Half the output quality.

Now I give it a focused question. It gives me a focused answer.

The second thing is prompt structure.

Theres research on how language models weight diferent parts of your context. The beginning and end matter most. The middle gets deprioritized. Same as humans.

I was doing it backwards. All my important context was buried in the middle.

Moving the most important thing to the top and the most important ask to the bottom changed my output imediately.

Three months into learning how to build with AI. The people ahead of me dont have better tools. They have better sistems around the same tools.

Thats the real gap.

3 LinkedIn Posts.
Ready to Copy.

The Admission

The Blunt One

The Curiosity Gap

Ready to Grow Your Online Presence?

3 LinkedIn Posts.Ready to Copy.

The Admission

The Blunt One

The Curiosity Gap

Ready to Grow Your Online Presence?

3 LinkedIn Posts.
Ready to Copy.