Who it is for
For researchers, grad students, and scientists who need to run literature reviews, wrangle datasets, and draft papers.
Knowledge & Research / Advanced
The AI Research Lab
Run literature reviews, wrangle datasets, and draft papers with AI you can defend.
The problem
It starts with the situation you're actually in.
A doctoral student in clinical psychology fed forty abstracts into a chatbot, asked for a synthesis, and pasted the result into a draft introduction. Three of the citations did not exist. The question is no longer whether to use AI in research.
Two graduate students in the same lab both used AI to screen abstracts last month. One kept a versioned prompt file, logged the model and date, and exported ASReview's decision log to the shared drive — and only one of them could prove what they did.
Outcomes
What you'll be able to do.
AI sharpens the question; the database defines the corpus
You learn to test any AI search assistant before trusting it. Ask it for "all randomized trials of spaced repetition in adult second-language learning since 2015," and you'll recognize the tidy paragraph of plausible studies for what it is.
The Citation That Wasn't There
You see how a reviewer at a mid-tier neuroscience journal flagged a submitted 2026 manuscript for a reference that did not exist — a DOI that resolved to nothing — so it never happens to yours.
The failure mode that defines the task
You can have a results section that is correct and analysis notes you can defend line by line, and still not have a paper. You learn to close that last gap.
The Question an Editor Will Actually Ask
You're ready when, three weeks after submission, a desk editor emails a single line: "Please describe any use of generative AI in the preparation of this work, per our policy.".
Inside the book
A closer look at the work inside.
Before you delegate any research task to AI
Have you classified the task as generation (produce candidates) or assertion (state facts)? Assertions get verified, always. Can you name the ground truth you'll check the output against — a source text, a known result, a manual spot-check?
The research lifecycle, stage by stage
- Have you classified the task as generation (produce candidates) or assertion (state facts)? Assertions get verified, always.
- Can you state the ground truth you will check the output against—a source text, a known result, a manual spot-check?
- Is the cost of an undetected error low enough to delegate, or high enough to supervise or keep manual?
- Have you recorded what you asked and what you used, so the step is reconstructable for a collaborator or reviewer?
- Does a human make the final inclusion, interpretation, or authorship call where it counts?
Build the first three entries of your prompt library
Do this once and you have a template for every project afterward. Take a prompt you used this week and rewrite it with explicit role framing, one hard constraint, and an uncertainty instruction. Run it twice at temperature ~0.2 to confirm the outputs are near-identical, then once at ~0.8 to watch the output widen.
The four controlled inputs of a prompt
Every prompt has parameters whether you set them deliberately or not. Make them explicit and you can vary one at a time, like any controlled experiment — starting with role framing, where telling the model who it's acting as constrains the distribution of likely outputs.
Case study: the irreproducible keyword set
You're scoping a review and you ask a general model to "suggest search terms for studies on the worked-example effect in undergraduate statistics." You get fourteen terms. Useful. You paste them into a database, get a reasonable yield, and move on — until you try to run it again.
The AI Research Lab frameworkInside the book
Visual preview
A diagram you can keep open while you work.
Table of contents
10 chapters, built to be read in order.
01
The Augmented Lab — Where AI Fits the Research Workflow
02
Prompting as Experimental Method
03
Scoping the Literature — Research Questions, Search Strategies, and Gap Detection
04
Screening and Synthesis at Scale
05
Reading Machines — Extraction, Claim Verification, and Citation Integrity
06
Wrangling Datasets — Cleaning, Transformation, and Exploratory Analysis
07
Modeling, Code, and Computational Reproducibility
08
Drafting the Paper — From Findings to Manuscript
09
Ethics, Authorship, and Disclosure
10
Building Your Lab's AI Operating System
164
Pages
14,394
Words
43
Exercises, checklists & tools
10
Chapters
Formats
Three formats. One purchase.
EPUB, PDF, and HTML are included so the book can work on an e-reader, as a designed copy, or as a searchable desk reference.
EPUB
For e-readers and reading apps.
The designed edition with diagrams and layouts intact.
HTML
Searchable, copy-pasteable, and practical as a reference.
Complete guide
The AI Research Lab
$12.99
Advanced guide pricing
- ✓EPUB + PDF + HTML included in one purchase
- ✓10 chapters from the complete guide
- ✓DRM-free files for your own devices
- ✓7-day refund review for duplicate purchases, access issues, wrong files, or materially defective downloads
Secure checkout / instant download / tax handled at checkout
Before you buy
Questions, answered.
Does this include the full book?
Yes. You get the complete edition, including the chapter sequence and internal materials described on this page.
Which formats are included?
EPUB, PDF, and HTML are included so you can read on an e-reader, keep a designed copy, or use the searchable browser version.
What is the refund policy?
Because this is an instant digital download, broad change-of-mind refunds are not offered after the files have been accessed. Refund requests are reviewed within 7 days for duplicate purchases, accidental purchases before access, access failures we cannot fix, wrong files, corrupted files, or pages that materially misdescribe the book.