Self-Improving
Legal Research
An AI research assistant that iteratively improves its legal analysis through deterministic evaluation, named mutations, and measurable score deltas.
How self-improvement works
Standard LLM
Query → Single LLM call → Answer
- xNo retrieval from real case law
- xCitations may be hallucinated
- xNo way to measure answer quality
- xOne shot -- take it or leave it
Self-Improving Pipeline
Query → Plan → Retrieve → Synthesize → Evaluate → Mutate → Loop
- +RAG over 1,000+ real court decisions
- +Citations verified against corpus
- +5-dimension LLM-as-judge scoring
- +Named mutations fix weak dimensions each iteration
Plan
Research strategy
Retrieve
Semantic search
Synthesize
Generate answer
Evaluate
5-dim scoring
Decide
Keep / discard
Mutate
Fix weakest
This system is for legal research assistance only -- not legal advice. All outputs include citations, confidence scores, and explicit warnings when reliability is low.
Cmd+Enter
Demo queries
Corpus coverage
20K+
India
SC + Delhi HC (AWS)
11K+
US
Harvard CAP + CourtListener
10K+
Canada
A2AJ / CanLII
5K+
UK
National Archives
50+
UAE
DIFC / BAILII
46K+
Real Cases
5
Jurisdictions
5
Eval Dimensions
Experimental
This is a self-improving AI research assistant that iteratively refines its legal analysis through deterministic evaluation, named mutations, and measurable score deltas. Watch the AI research, evaluate, and improve in real time.
Built by Luv Kapur