Domain-Expert Research Engine
Where ChatGPT is wrong, this is right. The Harvey-clone for your niche.
Build a research tool a domain expert would actually trust to do their job. Flagship pitch: legal precedent search for solo and small-firm lawyers. Westlaw is $400/month, lawyers want a cheaper option that actually works. The real market is $9B raised in 2025 alone (Harvey: $300M, Glean: $260M, Hebbia: $130M, Elicit, Casetext) — and there's massive room for niche verticals. Hybrid retrieval (BM25 + dense), cross-encoder reranking, query rewriting / HyDE, GraphRAG. Every improvement quantified with Project 3's eval harness — you ship with numbers, not vibes. The portfolio outcome: 3 real domain experts from your network using it to do their actual job.
The product in detail
The product solves the "ChatGPT is wrong about my domain" problem. The user is a working professional in a high-stakes field — law, medicine, finance, regulatory compliance — who can't afford to have an AI assistant make things up. They upload (or your product ingests) the authoritative documents for their domain, and from then on every answer is grounded, cited, and accurate enough to trust in their actual daily work. The flagship pitch: legal precedent search for solo and small-firm lawyers. Westlaw is $400/month and the interface hasn't changed since 2005. Lawyers want a cheaper, modern alternative.
The target user persona: a solo or small-firm lawyer who handles 5–15 active cases and needs to find relevant precedent fast. They're currently paying $400+/month for Westlaw or LexisNexis, or doing manual research that takes hours. Adjacent personas include nurse practitioners querying clinical literature for patient care decisions, retail investors analyzing 10-Ks and earnings calls, and compliance teams answering "is this configuration HIPAA-compliant" questions 20 times a week.
Real market: Harvey AI ($300M raised, Sequoia-led), Hebbia ($130M, used by hedge funds and law firms), Casetext (acquired by Thomson Reuters for $650M), Elicit ($9M for scientific research papers), PaperPal. Collectively over $1B raised in 2025 for domain-specific AI research tools. The pattern: pick a niche where accuracy and citations matter more than breadth. The winners are vertical, not horizontal.
Technically you upgrade Project 1's vanilla RAG into a production-accuracy retrieval system: hybrid retrieval (BM25 sparse + dense vector, fused with reciprocal rank fusion), cross-encoder reranking (two-stage: top-50 from hybrid, reranked to top-5 for the LLM), query rewriting and HyDE for ambiguous or jargon-heavy queries, and optionally GraphRAG for domains with strong entity relationships (case citations, scientific paper references, drug interaction graphs). Every improvement is quantified against Project 3's eval harness — you ship with metrics, not vibes.
What you ship: a research tool over a real domain corpus, used by 3+ real domain experts from your network to do their actual job. A public scorecard comparing every RAG variant you tried (vanilla → hybrid → reranked → HyDE → GraphRAG) with precision/recall/faithfulness numbers. A tech-blog post documenting when each approach wins and when it loses. When a recruiter for Harvey, Hebbia, or any vertical-AI startup sees this portfolio, they see someone who has already shipped what they're hiring for.
Pick what you'll build
This is one project with one skill graph — but you pick the product framing that fits your interests or career goals. Each option below is a viable portfolio piece. We'll narrow it together in the kickoff call.
- Legal case research — precedent search for solo/small-firm lawyers (Westlaw is $400/mo; market is wide open below that)
- Scientific paper discovery — your field's arXiv with citation-graph navigation (Elicit-clone for one niche)
- Medical literature Q&A — PubMed search for a specific clinical question (audience: nurse practitioners, junior clinicians)
- Financial report analyst — Q&A across 10-Ks, earnings calls, analyst reports (audience: retail investors)
- Regulatory compliance Q&A — GDPR, HIPAA, SOC2 question answering (audience: compliance teams at small SaaS)
- Production-accuracy codebase Q&A — when Project 1's vanilla approach isn't good enough for shipping
- Advanced RAG is a tacit must-know — Δ+3 with interviews 14% > posts 11%.
- The specific tested concepts (reranking, hybrid search, HyDE, GraphRAG, RAG-vs-CAG, 'lost in the middle') appear in 7+ interview questions but rarely as explicit JD bullets.
- The opportunity: build this and you can pass any RAG senior-engineer interview.
Research backing this project
Every project on projeckt.ai is calibrated against real job posts and real interview questions, not opinion. Here's the data behind this one.
- •Hybrid retrieval (interview-heavy, post-light)
- •Cross-encoder reranking
- •HyDE / query rewriting
- •GraphRAG / RAG-vs-CAG decision-making
Themes covered
Each chip shows how many of the 46 analysed job posts cite this theme.
Justified by real job posts
Verbatim quotes from the postings that drove the design of this project.
“Develop and optimize RAG pipelines, agent architectures, and LLM-powered systems”
“How do you handle the challenge of updating a RAG model's knowledge base without retraining the entire model?”
“You're building a customer support chatbot for a global company, and user queries are often multi-lingual. How would you use an embedding model to retrieve relevant documents in various languages…”
What you'll be able to do
- 1Implement BM25 sparse retrieval alongside dense vector retrieval and fuse rankings (reciprocal rank fusion)
- 2Choose between bi-encoder and cross-encoder rerankers and explain the quality / latency tradeoff
- 3Implement HyDE (Hypothetical Document Embeddings) query transformation
- 4Implement query rewriting for multi-turn conversation context
- 5Reason about chunking strategy: sliding window vs semantic vs document-structure-based
- 6Detect and address the 'lost in the middle' phenomenon in long contexts
- 7Compare Naive RAG vs RAG 2.0 quantitatively using Project 3's eval harness
- 8Decide when to use CAG (Cache-Augmented Generation) vs RAG
- 9Implement basic GraphRAG: entity graph + community detection + traversal-based retrieval
- 10Articulate when fine-tuning would beat RAG — backed by experiment data showing it doesn't (for your use case)
Tools you'll learn
Hands-on with these tools across the paid and open-source stacks. The Paid / OSS / Both label shows which stack each tool belongs to.
Week-by-week checkpoints
Each checkpoint is a Pull Request to your public GitHub repo. Rohan reviews on the PR; merge = checkpoint complete.
BM25 added alongside dense retrieval. Reciprocal rank fusion combines results. Eval harness shows Δ vs Project 1's baseline.
Hybrid beats dense-only on ≥2 of 4 metrics by >5%; eval scorecard published.
- •BM25 / sparse retrieval
- •Reciprocal rank fusion
- •Eval-driven retrieval decisions
Two-stage retrieval: top-50 from hybrid → cross-encoder rerank → top-5 to LLM. Eval shows further improvement. Latency cost measured.
Reranked top-5 beats fused top-5 on Faithfulness / Context Precision; latency increase quantified.
- •Cross-encoder rerankers
- •Two-stage retrieval
- •Quality-vs-latency tradeoff
Query rewriter for multi-turn context. HyDE transformation applied. Eval shows improvement on ambiguous queries.
Multi-turn test set passes; HyDE wins on technical-jargon queries.
- •HyDE (Hypothetical Document Embeddings)
- •Query rewriting
- •Multi-turn context handling
Optional GraphRAG over a structured corpus (codebase or research papers). Comprehensive write-up comparing every RAG variant — when each wins, when each loses. Blog post draft.
Write-up published as PR or blog; comparison table with eval numbers; clear 'use X when Y' guidance.
- •GraphRAG basics (entity graph + community detection)
- •Comparative analysis writing
- •Tech-blog craft
Pick your stack
Same skills, different credentials. Start free with the OSS stack or use the paid stack to learn directly on production tools.
| Tool | Purpose | License | Hardware | Market equivalent |
|---|---|---|---|---|
| Qdrant or Weaviate self-hosted | Hybrid retrieval (dense + BM25) | Apache-2.0 | Docker | Pinecone hybrid |
| BGE-reranker via Sentence Transformers | Cross-encoder rerank | MIT | CPU OK, GPU faster | Cohere Rerank |
| Ollama (Qwen 2.5 14B) | LLM | Apache-2.0 | 16GB+ RAM | GPT-4o / Claude |
| Neo4j Community Edition | Graph DB | GPL | Docker | Neo4j Aura |
| LlamaIndex | Pipeline composition | MIT | Free | Same |
Estimated cost: $0/month — fully self-hosted.
Interview questions you'll be ready for
Real questions from 13 sources, mapped to the skills this project builds.
Ready to start building?
Book a free 30-minute project planning session. We'll tailor this project to your stack, team, and goals.