What is Real Problem AI?

Real Problem AI is a curated directory of 100 real problems worth building an AI startup around. Each problem is scored honestly on severity, AI feasibility, market signal, and competition gap. We're for builders looking for what to build, not for ethics debates about AI.

What is the difference between Real Problem AI and AI ethics or AI risks indexes?

Real Problem AI catalogues problems AI can solve — productive, commercial opportunities for builders. AI ethics indexes catalogue problems with AI itself (bias, environmental cost, copyright, consciousness). Different intent, different audience: founders and indie hackers vs researchers and policy folks.

What should I build with AI in 2026?

Look for problems with high severity (it costs people hours, sleep, or revenue every week), high AI feasibility (LLMs, vision or voice are great at this today), strong market signal (Reddit threads + willingness to pay), and a clear competition gap (incumbents miss the same obvious thing). Real Problem AI scores 100 such problems for you on those four axes.

Where do Real Problem AI's startup ideas come from?

From three sources, scanned each cycle: 1) Reddit threads where people describe a real friction in their own words, 2) podcasts and talks of founders young builders trust (Nikhil Kamath, Aman Gupta, Ankur Warikoo, Raj Shamani, Andrej Karpathy, Greg Isenberg), and 3) a continuous 1,000+ source scan covering forums, app store reviews, regulator filings, and field interviews.

Is Real Problem AI free?

Yes. Browsing every problem, opening every score breakdown, raising your hand on a co-founder match, and the founder vault are all free. There is no paywall.

How is each problem scored?

Four axes, each 1-10, weighted: Problem Severity (30%), AI Feasibility (25%), Market Signal (25%), Competition Gap (20%). Weighted average is the Opportunity Score on each card. Only problems clearing 7.0+ make the live list.

Why do my RAG search results look correct but the answer is still wrong?

Retrieval shows the right chunks in the trace. The LLM still produces a hallucinated, slightly wrong, citation-broken answer. Debugging is guesswork.

Category: AI / Agents · Trend: RAG · Opportunity score: 7.7 / 10

What is the “Why do my RAG search results look correct but the answer is still wrong?” problem in 2026?

Retrieval shows the right chunks in the trace. The LLM still produces a hallucinated, slightly wrong, citation-broken answer. Debugging is guesswork.

Who has this problem?

Engineers running customer-facing RAG (support bots, internal search, doc Q&A).

Recorded source context

Dataset source note: Top 5 chunks contain the exact answer. The model says "I don't know." Or worse, makes up a plausible wrong answer. This burns a senior eng day a week.

This note may summarize the referenced material rather than quote it verbatim. Source label: Twitter/X RAG-failure threads (Hamel Husain, Jason Liu), r/MachineLearning, LlamaIndex GitHub issues..

Existing players in this space

Ragas: Evals but mostly offline
LangSmith: Traces, weak on retrieval diagnostics
Arize Phoenix: Closer; setup heavy

What existing players are missing

RAG-specific drift detection: side-by-side of "retrieved evidence" vs "model answer", auto-flag when citations don't appear in the answer or vice versa, and a regression test set seeded from real failures.

How Real Problem AI scores this opportunity

Aggregate score: 7.7 / 10. Four-axis rubric:

Problem severity: 8 / 10
AI feasibility today: 8 / 10
Market signal: 8 / 10
Competition gap: 6 / 10

How to build a solution: stack hints

Embedding-based fact alignment scorer
Citation-extraction NLP layer
Continuous eval pipeline tied to traffic
Slack/PagerDuty alerts on grounding failure