A clean PDF library

A folder structure with consistent filenames, separated source types, duplicate checks, and clear priority levels.

A literature matrix

A spreadsheet that captures citation details, methods, AI use, claims, evidence strength, limitations, and relevance.

Conceptual streams

A set of review streams that organize the literature around meaningful distinctions, not just keywords.

An evidence audit

A way to trace each major claim back to source material before using AI to draft or refine synthesis.

Stage 1

Collect and organize PDFs

Create a clean source library with consistent filenames, folder categories, and priority labels.

Stage 2

Extract metadata and core claims

Capture citation details, abstract, method, contribution, limitations, and evidence type.

Stage 3

Build the literature matrix

Turn article-level notes into a structured spreadsheet that supports comparison and review decisions.

Stage 4

Tag streams and methodological approaches

Classify papers into provisional conceptual streams and mark borderline cases.

Stage 5

Audit the evidence

Check whether each claim is supported by appropriate evidence, and whether the wording is cautious enough.

Stage 6

Feed selected evidence into AI tools for synthesis

Use structured evidence, not raw document overload, as the basis for AI-supported literature review drafting.

Step 1

Define the pipeline goal

Before organizing files, define what the pipeline is supposed to produce. A pipeline for a conceptual review will look different from one for a systematic review, scoping review, dissertation chapter, or evaluation report.

AI prompt:

I am building an AI literature review pipeline for [topic]. The review type is [type]. The final output should be [output]. Help me define the pipeline goal, required inputs, expected outputs, risks, and quality checks before I begin organizing sources.

Output of this step: a pipeline brief that explains why you are building the pipeline and what the pipeline must produce.

Step 2

Organize your PDF folder

A literature review pipeline begins with file discipline. If the folder is messy, the later AI work will also be messy. Use folders and filenames that make papers easy to locate, check, and cite.

AI prompt:

Here is a list of filenames from my literature folder. Identify duplicates, unclear filenames, missing years, inconsistent naming patterns, and papers that may need manual checking before I build my literature matrix.

Output of this step: a clean folder with consistent filenames and a note explaining what each subfolder contains.

Step 3

Create the literature matrix spreadsheet

The literature matrix is the centre of the pipeline. It converts a folder of PDFs into structured information that can be searched, filtered, compared, audited, and safely used with AI tools.

AI prompt:

Help me design a literature matrix for a review on [topic]. The matrix should support synthesis, evidence auditing, and conceptual stream development. Suggest columns, definitions, allowed values, and examples.

Output of this step: a spreadsheet template that becomes the master control document for the review.

Step 4

Extract metadata and core claims

Use AI tools for extraction, but keep the output structured. The goal is not to summarize each paper beautifully. The goal is to capture comparable information across papers.

AI prompt:

Extract the following fields from this paper: citation, paper type, method, data source, AI role, core claim, evidence type, evidence strength, limitations, conceptual vocabulary, possible stream, and relevance to my review. If the paper does not provide enough information, write unclear rather than guessing.

Output of this step: one structured row per paper in the literature matrix.

Step 5

Tag conceptual streams and methodological approaches

The stream tags are what turn the matrix into a review structure. These tags should be analytical, not merely descriptive. A paper should not be assigned to a stream only because it uses a similar keyword.

AI prompt:

Using this literature matrix, propose conceptual stream tags and methodological approach tags. For each tag, define inclusion criteria, exclusion criteria, strong examples, borderline cases, and papers that should not be forced into the tag.

Output of this step: tagged papers with confidence levels and notes on borderline cases.

Step 6

Use source-grounded tools for summaries and comparisons

Tools such as NotebookLM and other source-grounded systems are useful when you need to ask questions across a defined document set and check where an answer came from. Use them to support extraction and comparison, not to replace your judgment.

AI prompt:

Compare these papers only on the role assigned to AI. For each paper, identify whether AI is treated as a tool, assistant, collaborator, evaluator, or interpretive partner. Cite the source location or provide the relevant passage summary where possible.

Output of this step: focused comparisons that can be added to your matrix or used to refine stream tags.

Step 7

Use simple Python checks for filenames, metadata, and duplicates

You do not need an advanced programming workflow. Python is optional in this tutorial. A few simple checks can help you find duplicate PDFs, inconsistent filenames, and files that need manual review before you ask AI to process them.

from pathlib import Path
folder = Path("papers")
pdfs = sorted(folder.glob("*.pdf"))
for pdf in pdfs:
    name = pdf.stem
    parts = name.split("_")
    has_year = any(part.isdigit() and len(part) == 4 for part in parts)
    if len(parts) < 3 or not has_year:
        print("Check filename:", pdf.name)

from pathlib import Path
from collections import defaultdict
folder = Path("papers")
by_size = defaultdict(list)
for pdf in folder.glob("*.pdf"):
    by_size[pdf.stat().st_size].append(pdf.name)
for size, files in by_size.items():
    if len(files) > 1:
        print("Possible duplicates:")
        for file in files:
            print(" -", file)

from pathlib import Path
import csv
folder = Path("papers")
rows = []
for index, pdf in enumerate(sorted(folder.glob("*.pdf")), start=1):
    rows.append({
        "paper_id": f"P{index:03d}",
        "filename": pdf.name,
        "file_size": pdf.stat().st_size,
        "status": "to_review"
    })
with open("literature_inventory.csv", "w", newline="", encoding="utf-8") as file:
    writer = csv.DictWriter(file, fieldnames=["paper_id", "filename", "file_size", "status"])
    writer.writeheader()
    writer.writerows(rows)

Practical note:

These scripts do not analyze the literature. They help you create a cleaner, safer input set for later AI-supported analysis.

Step 8

Audit evidence before synthesis

The evidence audit protects your review from overclaiming. Before asking AI to write a section, check whether each claim has enough support and whether the support is empirical, conceptual, methodological, or speculative.

AI prompt:

Using this literature matrix, create an evidence audit table. For each major claim, identify supporting papers, evidence type, evidence strength, possible counterexamples, and whether the claim needs cautious wording.

Output of this step: an evidence audit table that should be reviewed before drafting begins.

Step 9

Feed selected structured evidence into AI tools

Only after the matrix, tags, and evidence audit are ready should you ask AI to help with synthesis. Feed the AI selected evidence rather than the entire uncontrolled folder.

AI prompt:

Draft a synthesis section using only the structured evidence below. The section should argue [argument]. Use the papers in [stream]. Do not add sources or claims that are not in the evidence table. Distinguish strong evidence from emerging or speculative claims. End with a transition to [next section].

Output of this step: a draft synthesis section that remains traceable to your matrix and audit table.

Step 10

Document your process

A good pipeline leaves a trail. Document how papers were selected, how AI was used, what was checked manually, and how the final synthesis was developed.

AI prompt:

Help me write a transparent methods note for this AI-supported literature review pipeline. Include what AI was used for, what was checked manually, how sources were organized, how evidence was audited, and what limitations remain.

Output of this step: a methods note that can be adapted for a paper, chapter, thesis, or evaluation report.

Weak workflow

Upload 100 PDFs. Ask AI to write a literature review. Read the output. Edit the prose. Hope that the model understood the field correctly.

Stronger pipeline

Organize PDFs. Build a matrix. Extract core claims. Tag streams. Audit evidence. Select only relevant rows. Ask AI to draft a section using the structured evidence. Review manually.

1. Pipeline planning prompt

Help me design a literature review pipeline for [topic]. Define the stages, inputs, outputs, quality checks, and risks. Do not write the review yet.

2. Matrix design prompt

Design a literature matrix for this review. Include columns for source details, method, AI role, core claims, limitations, evidence strength, conceptual stream, and audit notes.

3. Extraction prompt

Extract fixed fields from this paper. Use the matrix columns. If information is missing or unclear, mark it as unclear. Do not infer beyond the paper.

4. Stream tagging prompt

Suggest conceptual stream tags for these papers. Define each tag, identify strong examples, and mark borderline cases.

5. Evidence audit prompt

Create an evidence audit table. For each claim, list supporting papers, evidence type, evidence strength, possible counterexamples, and recommended wording.

6. Synthesis prompt

Draft a synthesis paragraph using only the structured evidence below. Do not introduce new sources. Distinguish strong evidence from emerging claims.

Internal EvalCommunity tutorial

Start with the companion tutorial if you want the broader research and writing workflow before building the pipeline.

How to Use AI for Literature Reviews

Official AI tools

• NotebookLM for source-grounded document work.
• ChatGPT for planning, drafting, critique, and synthesis support.
• Claude for long-form reasoning, document review, and writing workflows.
• Gemini for research, writing, planning, and comparison tasks.
• Python for optional folder, filename, duplicate, and CSV inventory checks.

Do I need Python to build an AI literature review pipeline?

No. You can build the pipeline with folders, spreadsheets, and AI tools. Python is optional, but it helps with duplicate checks, filename checks, and simple inventory creation.

Should I upload all PDFs to an AI tool at once?

Usually not. A more reliable approach is to structure the evidence first, then provide selected matrix rows and audit notes to the AI for synthesis.

What is the role of NotebookLM in this pipeline?

NotebookLM and similar source-grounded tools can help you ask focused questions across a document set, check where information appears, and compare papers before adding findings to your matrix.

How does this pipeline reduce hallucination?

It reduces hallucination risk by giving AI structured, selected evidence rather than asking it to infer from a large uncontrolled document set. It also requires human auditing before drafting.

Can this pipeline be used for qualitative research?

Yes. It is especially useful for qualitative research because it helps preserve distinctions between coding-centred, interpretive, dialogic, methodological, and conceptual approaches.