Study Guides |

Guided Solutions

Sun, 11 Oct 2026 00:00:00 +0000

Guided Solutions brings three contributions to a problem set together: an AI-written derivation, space for my own intuition, and a proof developed through tutoring. Each has its own labelled block, so I can compare approaches and see whose reasoning I am reading.

The separation matters when the routes differ. In the recorded microeconomics run, the agent supplied detailed reference proofs; later tutoring on two questions produced more compact proofs following the student’s approach. Both were kept, with room for the student’s interpretation alongside them.

An illustrative layout, rather than an excerpt from the private coursework, looks like this:

My reasoning: I think adding another option changes the chance of choosing the default, but I need to identify why.

Agent derivation: Work through the choice probabilities under the question’s stated rule, showing the intermediate steps.

After tutoring: Record the proof developed during the discussion and compare its key step with the reference derivation.

To prepare a document, I use the assignment and a question-by-question hints sheet to organize the work. The make lecture notes available to the , which finds the course’s definitions and notation alongside textbook passages. The agent is instructed to show its algebra and assumptions, while leaving the student’s writing intact. A later audit checks the document’s structure and mathematical work.

This is a procedure I direct using the existing conversion, search, and tutoring tools. The records one completed problem-set run and its tutoring follow-up. It provides a playbook for another assignment; each new solution still needs review. The course documents remain private, and the workflow has no dedicated automated test suite.

Study Guide Pipeline

Sun, 11 Oct 2026 00:00:00 +0000

The Study Guide Pipeline builds a guide from selected course materials, then lets me review proposed edits before they change the text. Topics can draw on named textbook sections, particular lecture notes, or newly discovered passages. Keeping that source selection explicit makes it easier to investigate a gap in the finished guide.

One early run exposed a useful mismatch: a requested textbook section did not cover the statistical test it was supposed to explain. The stronger expanded guide described what that section actually contained and developed the topic from other relevant material. That was more useful than repeating the requested reference as though it were correct.

From materials to a reviewed guide

I start with a topic list, instructions, and rules for choosing sources. The supplies passages from the converted and . Those passages need to be in the search catalog before guide planning can use them.

The tool assembles a source plan; newly discovered passages normally wait for review before drafting. The draft records which passages were supplied for each topic, and an optional can develop explanations and worked examples further. For tracing relationships between ideas rather than reading a topic in sequence, uses the same source foundation in a separate drafting workflow.

Revision is a separate step. It proposes specific changes for relevance, repetition, correctness, and organization. I can accept or reject them individually. Applying the decisions writes a new guide and checks that untouched passages remain unchanged and that no new page citations have appeared. It also refuses to apply an old review if the guide has changed underneath it.

That approach makes a long guide easier to refine without asking the model to regenerate every section. Spending limits can stop a run between sections or review tasks, and saved revision work can be resumed when its inputs are unchanged.

What changed in practice

In the first econometrics comparison, three draft-only variants were too short and conceptual for the intended study use. The expanded version supplied deeper derivations and examples, though it still lacked some of the exam-focused structure I wanted. Later topic-based drafts improved the worked problems but also wandered into irrelevant software discussion.

A subsequent review of one draft accepted 27 of 30 proposals. Applying the review made 18 text edits; other accepted items included advisory notes. The run reduced repetition, repaired headings, and added cross-references. An accepted warning can still leave a mathematical issue unresolved when it calls for investigation rather than supplying a correction.

A later microeconomics guide made the remaining gap clear: comparison against the assignment found missing material and errors the revision pass had missed. That check led to hand repairs. The and record the observations; the documents the current workflow. A generated guide still needs that final coverage and correctness review before I rely on it.

Summary Enhancement

Sun, 11 Oct 2026 00:00:00 +0000

A short summary can name a result without giving me enough detail to use it. Summary Enhancement returns to the exact textbook and note passages recorded by an existing AI-generated summary, then develops fuller explanations by topic. It can also add worked examples whose arithmetic is computed by running code.

The useful expansion is in the reasoning between the result and its application. For a statistical test, for example, a section can explain its assumptions, derive the formula, and walk through a small numerical calculation. An introductory description of what a test measures can then become something I can work through step by step.

The starting document can be a saved , or a draft from the . It needs the recorded passage references those tools write. The enhancer uses them to reopen the same material in the , rather than search again and possibly choose different evidence.

The tool can plan the topics itself or use a list I supply. It writes a separate expanded guide, leaving the original intact. Long equations get their own lines and long paragraphs are split to make the result easier to read.

One important distinction is preserved behind the reading text: which paragraphs draw on the sources, which add broader explanation, and which contain generated examples. A source map records the passages used by each section. This keeps the main text readable while giving me a way to inspect where its explanations came from.

Those records support review; they do not establish that every claim follows from its source. The validator checks that source labels exist, and code execution checks the example’s arithmetic. A person still needs to inspect the reasoning and interpretation around those calculations.

This is useful when I already have the right sources but need a fuller explanation. Starting a guide from a topic list also requires source selection and drafting, which the broader Study Guide Pipeline handles. The documents the current interface; the linked status record describes earlier implementation and testing.

A Long Study Guide Still Needs to Answer the Right Questions

Fri, 09 Oct 2026 00:00:00 +0000

The first microeconomics run of my study-guide pipeline produced eleven topics of explanations, but missed material needed for one of the assignment questions. It also imported language about statistical tests from an earlier econometrics prompt. A guide can be substantial, well organized, and still leave the reader unprepared for the problem in front of them.

There was a useful advance here as well. The could now assemble a guide from an explicit source plan, expand a chosen topic, and propose revisions for review. I could inspect what it planned to read and decide which edits to keep, instead of asking for a complete rewrite and trying to spot everything that had changed.

That source plan connects the visible study aid to the less visible infrastructure. The prepare textbooks and course notes; the locates passages within them. For a guide, I can name particular sources and sections as well as request a bounded search. Newly discovered passages normally wait for review before drafting.

Revision works on specific changes. It can suggest removing an irrelevant passage, consolidating repeated explanations, correcting a claim, or reorganizing a section. Applying accepted edits creates a revised copy and checks that untouched blocks remain unchanged. It also refuses a review if the guide has changed since the review was prepared. Those checks make edits traceable; mathematical judgment still has to come from examining the content.

The microeconomics run made that distinction concrete. Its audit identified several real issues, but some were recorded as notes rather than supplied with corrections. Accepting a warning did not repair the underlying argument. A later comparison with the assignment found additional errors, including a worked example whose construction could not demonstrate what it claimed. Missing coverage had to be written by hand and checked numerically.

The finished guide became usable through that combination of generated material, reviewed changes, and hand repairs. It also exposed a generalization problem: a prompt built around one statistical topic had carried assumptions into another subject. A more neutral prompt and course-specific relevance settings were follow-up work at this point.

The practical payoff is a guide I can investigate. If a topic is thin, I can examine the source plan. If a paragraph changes, I can inspect the proposed edit. If the guide is meant to prepare me for an assignment, that assignment supplies a final coverage check. Each makes the next revision more focused than simply asking the model for a better guide.

Retrospective account prepared October 16, 2026, covering the October 9 milestone. The and distinguish implemented steps from proposed improvements. Project links include later developments.