HeapFile Guide
How To Evaluate AI Memory Without Exposing Your Project
An AI memory tool should help the next work session recover the right information. A long history of saved conversations is useful only when the current task receives context that is relevant, current, and appropriate to share.
You can test that workflow with a small invented project before connecting private work. The exercise below is a suggested evaluation, not a HeapFile benchmark or a report of measured results.
Start With A Small Sample
Use a fictional reading-list project. Give it a clear goal and a few facts:
| Field | Sample content |
|---|---|
| Goal | Keep a short reading list with an understandable empty state. |
| Decision | Start with a single list because the first version is for one reader. |
| Constraint | Do not add shared accounts to this sample. |
| Open question | Should finished items stay visible or move to a separate view? |
| Next task | Compare two empty-state messages before adding more features. |
For the first pass, tentatively keep finished items visible while you evaluate the open question. Label that choice tentative, not final.
Add one checked result only after performing the check. For example, if you tested that a list entry remains after refresh, record how you checked it. Until then, call it an expected behavior or an open question.
Leave real account details, customer material, credentials, private files, and proprietary code out of the sample.
Check What A Fresh Session Receives
End the first session and start another through the workflow you intend to use. Ask it to state the goal, explain the decision, identify the constraint, and name the next task.
Compare its answer with the sample facts. Look for missing facts, invented details, and unrelated material. A convincing answer is not enough: you should be able to tell where the important context came from.
Also record the work required to make that context available. Did the new session receive it through the tool's supported process, or did you have to paste it in yourself? Both can be useful, but they are different workflows.
Persisted notes and the active context window serve different roles. Anthropic describes structured notes as information kept outside a session's context window and retrieved for later work. That distinction is useful when evaluating what a memory tool actually supplies. Context engineering guidance.
Change A Decision
Update the sample: completed items should move to a separate view. Record that this replaces the earlier tentative approach, and keep the reason for the change.
Start another session and ask what to implement next. Does it use the current decision? Does it recognize the earlier option as superseded, or present both as equally current?
Repeat with an unanswered question. A useful handoff should preserve uncertainty instead of turning a tentative idea into an established fact.
Compare Against Your Current Method
Run the same sample with a short note or handoff file. Keep the task, facts, and questions the same so the comparison means something.
| Method | What to inspect |
|---|---|
| Continuing the same chat | Whether the relevant decision remains available among the accumulated conversation. |
| Saving a short handoff | Whether you can keep it current and supply it to the next session consistently. |
| Using an AI memory tool | What it retrieves, how you review that context, and how much preparation it requires. |
Record observations rather than estimates: which facts arrived, which were missing, what had to be corrected, and what steps you performed. One small trial can expose a workflow problem; it cannot establish a broad productivity or cost claim.
Review Where Context Goes
Local storage and AI processing are separate questions. Before trying sensitive work, check the selected provider, the context being supplied, and the permissions available to the task.
Ask how you can review or change stored information, how supported handoffs move between tools, and what happens when a provider is unavailable. If the answer is unclear, keep that part of the evaluation marked unknown.
Use the product's current plan and system requirements when testing. A feature shown on a website may depend on the selected plan, installed version, or authorized connection.
Decide From The Observations
The tool earns a place in your workflow when the next session receives useful context with an acceptable amount of preparation and review. It should be easier to explain what carried forward, what changed, and what still needs checking.
HeapFile focuses on local-first AI memory and coordination for builders working across sessions. Start with the same sample questions rather than assuming a feature label answers them. Explore HeapFile or share an access or workflow question through HeapFile support.