Coding Agent Log to Blog Post
Using stored chat history as context to create richer more complete blog posts drafts
I built a working promo video draft in 30 minutes. But I got another valuable asset for free; A complete transcript of the AI conversation that produced it.
The code tells you what I built. The conversation tells you why and how. It’s a log of the false starts, the creative corrections, decisions, and dead ends. It's a log of the process.
This is how I turn that raw log into a polished blog post.
(Tip: I use the SpecStory Extension to automatically capture every chat. 0 effort for me to get that.)
From Raw Log to Insights
A raw transcript is just a data dump. The data dump contains all the details I need, and more. I use Gemini's large context window to upload the entire chat history to ground the first draft in what actually happened.
1. Pinpointing Critical Feedback
Human expertise is critical to my process. The chat log provided the exact prompts I used to direct the AI, making the examples real and concrete.
For example, syncing an animation to a musical cue is a matter of taste. The transcript gave me the quote for my blog post, which is stronger than paraphrasing from memory (or forgetting about that detail entirely because I have the memory of a goldfish and lots happened between then and now!).
From the AI Chat Log:
User
I want "Simple Air Application" to come in right at 35 seconds to match the major phrase change in the song.
How it appeared in the blog post:
This is a level of creative direction AI can't do on its own. It can't "feel" the music. It needs a human to provide the keyframe.
2. Showing the Process
Developers want to see real details. Using the history log offered details of the steps I actually took. When explaining how I gave the AI context for the video, I could show the exact tool call from the transcript.
From the AI Chat Log:
Tool use: mcp__air__fetch_air_documentation
3. Building a Narrative from Key Decisions
The chat log became the outline for my article. I used gemini to pull out and organize the key steps like:
Aesthetics: When AI generated text to was hard to read
Fact-Checking: Removing incorrect air.test module hallucination
Creativity: Syncing animation to musical cue
Value: Asking for more code and annotations of that
These moments became the topics in my blog post.
My Workflow
The transcript means I am more efficient. Here’s what I do:
Do the work. I use an AI to build, while SpecStory logs the session.
Upload the transcript to Gemini. The log is one of the primary sources of information
Add my opinions. I transcribe my feelings and opinions as additional context
Edit and refine. I do editing passes (both with help of AI and manually) to tighten the language and ensure it fits my voice.
The transcript is a chronological and accurate record of what happened I can rely on. And records are much better to rely on that memory
Having a clear process is the difference between simply using AI and truly directing it. If you're looking to build advanced, reliable AI workflows for your own team, this is the kind of practical skill we teach in our Elite AI Assisted Coding course.