Workflow
AI Product Requirements Workflow
Turn a fuzzy business need into requirements a team can build from — interrogate the need into concrete requirements, shape them as user stories, and write the PRD.
Build an AI Research Assistant with AI
The full path to an AI research assistant — define its scope, organize the source corpus, ground responses in references, extract key facts, synthesize findings, check groundedness, then validate it for use.
Overview
A research assistant is more than retrieval. A RAG system fetches passages and answers a question; a research assistant has to read across a body of sources, pull the facts and claims that matter, and synthesize them into findings someone can act on — with the citations to back each one and an honest account of what the sources don't say. This project builds that assistant in order: it frames the scope and the users, organizes the source corpus so the material is workable, grounds responses in real references instead of the model's memory, extracts the key facts, claims, and citations, synthesizes them into research outputs that hold together, then evaluates the result for groundedness and hallucination risk before validating the assistant for real use. It is deliberately not a static knowledge site (that's the Knowledge Base project) and not a publishing pipeline (that's the AI Content Engine) — it's an assistant workflow over sources whose output is grounded synthesis. NewPrompt designs the assistant and generates the prompts, grounding contracts, and evaluation sets for each stage; you run those in your own AI tool and own the sources and the conclusions. It does not do the research for you or run AI on its servers — it gives the build a grounded, evaluable structure instead of an ungrounded 'summarize this' prompt.
The journey
Each stage runs a NewPrompt workflow, with a supporting resource and tool. Work them in order — the output of each stage feeds the next.
See the execution map →Define & Scope
Clarify what you're building and for whom.
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Define the research assistant scope
Frame what the assistant is for and who uses it — the research questions, the kinds of sources, and what a good answer looks like — so the rest of the build serves a defined job instead of a vague 'help me research'.
Outcome A defined scope: the users, the questions, and what a good grounded answer is.
Design
Design the right solution before building.
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Organize the source corpus
Make the body of sources workable — packaged with clear boundaries and labels — so the assistant reads from organized, citable material instead of a heap of raw documents.
Outcome A source corpus organized and packaged for the assistant to work from.
Build & Refine
Build, test, secure, and make it production-ready.
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Ground responses in source context
Build the grounding contract that ties answers to the sources — cite what the material says, and say plainly what it doesn't — so the assistant reasons from references instead of from the model's memory.
Outcome A grounding contract that anchors responses to the sources, with no invention. -
Extract key facts, claims, and citations
Pull the facts, claims, and figures that matter out of the sources, each tied to where it came from, so synthesis works from a clean set of cited evidence rather than re-reading everything.
Outcome Key facts and claims extracted, each tied to its source. -
Synthesize findings into research outputs
Reconcile the evidence across sources into findings that hold together — agreements, conflicts, and gaps named — so the output is a synthesized read, not a list of disconnected quotes.
Outcome Synthesized findings that reconcile the sources, with conflicts and gaps named. -
Evaluate groundedness and hallucination
Check that the findings actually trace to the sources — catch the confident claim with no citation behind it — so the assistant ships on evidence it stays grounded, not on a good-looking demo.
Outcome Findings checked for groundedness, with hallucinated claims caught.
Ship & Validate
Ship with confidence and validate results.
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Validate and prepare for use
Measure whether the assistant actually helps its users research faster and more reliably — the success signal — and decide if it's ready, before relying on it for real work.
Outcome The assistant validated against its success signal and readied for use.
Expected outcome
An AI research assistant designed end to end — a defined scope, an organized source corpus, grounded responses, extracted facts and citations, synthesized findings, a groundedness and hallucination evaluation, and a validation gate — so the assistant produces research someone can trust and trace, not confident summaries with no sources behind them.
Best for
- Synthesizing grounded findings from a body of sources
- Research workflows that need citations and a groundedness check
- Turning a source corpus into usable, traceable research outputs
Not for
- A static, browsable knowledge site — that's the Knowledge Base project
- Retrieval Q&A infrastructure — that's the RAG System project
FAQ
How is this different from the RAG System project?
RAG is retrieval infrastructure — chunk, ground, retrieve, answer. This is the assistant on top: it reads across sources, extracts the facts that matter, and synthesizes findings with citations. The synthesis and groundedness evaluation are the core, not the retrieval plumbing.
How is this different from the Knowledge Base or AI Content Engine?
A knowledge base is a static, browsable site; the content engine is a publishing pipeline. This is an assistant workflow over sources whose output is grounded synthesis — research someone can act on and trace, not pages to publish.
Does NewPrompt do the research?
No. NewPrompt designs the assistant and generates the prompts, grounding contracts, and evaluation sets. You run them in your own AI tool, supply the sources, and own the conclusions. NewPrompt does not run AI or gather sources for you.
How do I build an AI research assistant?
Build it in order across seven stages: define the assistant's scope and users, organize the source corpus, write a grounding contract, extract key facts and citations, synthesize findings that reconcile the sources, evaluate groundedness and hallucination risk, then validate against a success signal before real use. You run each stage in your own AI tool.
What sources do I need before building an AI research assistant?
You need the body of source documents the assistant will read and cite — plus a defined scope naming the research questions and what a good grounded answer looks like. The first two stages frame that scope, then organize the corpus with clear boundaries and labels so the assistant works from citable material, not a raw document heap.
How do I make an AI research assistant cite sources reliably?
Reliable citation comes from three stages working together: a grounding contract that ties every answer to the sources and states plainly what they don't say, fact extraction that binds each claim to where it came from, then a groundedness and hallucination evaluation that catches confident claims with no citation behind them before you rely on the assistant.
Workflows in this project
Workflow
AI Long Document Analysis Workflow
Get AI to actually read a document that's too big for one prompt — fit it to the model, split it cleanly, package the parts, and analyze them without losing the thread.
Workflow
AI RAG Context Workflow
Prepare documents for a RAG system so retrieved answers stay accurate — budget the chunk size to the model, ground the sources against drift, and split them on clean boundaries for retrieval.
Workflow
AI Data Extraction Workflow
Turn messy text into structured data you can trust enough to feed another system — bound the source, extract the fields, force clean JSON, and validate before it flows downstream.
Workflow
AI Research Synthesis Workflow
Pull a single coherent view out of a stack of sources — package them together, summarize each faithfully, then have AI synthesize across them instead of one at a time.
Workflow
AI Agent Evaluation Workflow
Find out whether an AI agent behaves before users do — define what correct means, build test scenarios with expected outputs, catch failures and hallucinations, then regression-test each version.
Workflow
AI Product Validation Workflow
Find out whether the thing you shipped actually worked — define the success metric, plan the measurement, classify the real evidence, then render a verdict and an iterate / pivot / scale decision.
Resources used in this project
Resource
Product Requirements Assistant
Turn a rough feature idea into a structured requirement: problem statement, acceptance criteria, and what's explicitly out of scope.
Resource
Package Long Documents for AI — Delimiters and § Labels
Pasting a document raw mixes material with instructions. Package it: explicit delimiters, citable [§N] section labels, and grounding rules — the source travels verbatim.
Resource
Reduce AI Hallucinations with Grounding — the Strict Contract
The strongest anti-hallucination tool is structural: only the delimited source exists, gaps answer "The source does not say.", and uncited claims are forbidden.
Resource
Extract Data From Text with AI
Free text in, named fields out. The extraction prompt pattern that turns any unstructured text into consistent, parseable records.
Resource
Research Synthesis Assistant
Extract and compare findings from multiple sources without collapsing them into a single blended perspective.
Resource
Hallucination Detection Prompt
Catch the confident invention — check an AI output's claims against its source and flag every statement that isn't supported, with the unsupported span quoted.
Resource
Product Validation Report Prompt
Consolidate the whole validation into one shareable report — the success metric, the evidence, the hit / partial / miss verdict, and the iterate / pivot / scale decision, in a form the team and stakeholders can act on.
Tools used in this project
Tool
System Prompt Generator
Build structured system prompts from role, tone, constraints, and model target.
Tool
Long Input Formatter
Package source material with delimiters, citable section labels, and grounding rules — material and instructions stay separate.
Tool
Extraction Prompt Generator
Build prompts that extract defined fields from unstructured text — emails, invoices, tickets, résumés.
Tool
AI Output Validator
Paste an AI's output and validate it against the expected format — with a repair prompt for every failure found.
Tool
Markdown Output Builder
Build prompts that produce documents in a fixed structure — headings, sections, and tables.
Recommended next project
Project
Build an AI Content Engine with AI
The full path to a content operation that runs, not a pile of posts — set the editorial strategy, research the topics, build a reusable template, then produce and QA structured pieces on repeat.
Related projects
Project
Build a RAG System with AI
The full path to a retrieval system that returns grounded answers — understand the corpus, chunk and ground it, extract and classify the metadata, then evaluate that retrieval actually works.
Project
Build a Knowledge Base with AI
The full path to knowledge that's findable by people and AI — plan the taxonomy, structure it for search, write the articles, tag the metadata, make it retrievable, then ship it maintainable.
Project
Build an AI Document Processing System with AI
The full path to an AI document processing system — define the use case, design the intake pipeline, extract fields from unstructured documents, classify and route them, pin the output contract, evaluate accuracy, then ship it monitored.