SpeechXP
A Chat-Driven App for speech coaching: users converse with an LLM persona, and structured model outputs grade their delivery in real time on clarity, tone, confidence, and relevance.
Chat-Driven AppPostgreSQLPrismaJWTStable DiffusionNext.js
Key wins
- ▸10,000+ synthetic character images generated with Stable Diffusion
- ▸Real-time scoring on clarity, tone, confidence, relevance
- ▸JWT-protected API endpoints, PostgreSQL + Prisma data layer
As a Chat-Driven App
SpeechXP uses the Chat-Driven App pattern to grade real-time dialogue practice. The LLM scores the user’s reply to a simulated high-pressure scenario and emits structured outputs the app turns into a graded dashboard and formatted feedback.
User input
Scenario: explain a missed project deadline to a frustrated client. User reply: "I want to be straight with you — we slipped because of an unexpected dependency change. Here’s the new plan and the date I’ll commit to."
LLM output
Strong, accountable opening. Watch the pace on the recovery plan. [ACTION:show_grade clarity="8" tone="7" confidence="9" relevance="9"] [ACTION:format_feedback section="strengths" body="Owned the slip, offered a concrete recovery."] [ACTION:format_feedback section="growth" body="Slow down when delivering the new commitment date."] [ACTION:advance_to next_scenario="2"]
What the app does
SpeechXP parses each tag and renders the score breakdown, lays the feedback into the strengths/growth sections, and queues the next scenario in the practice loop — all driven from the chat surface, not separate UI controls.
What I built
- Built an AI speech-coaching product that grades user performance in difficult dialogue scenarios and helps users practice high-pressure conversations.
- Designed the product around real-time feedback, scoring, repeatable practice loops, and measurable performance improvement rather than passive chatbot interaction.
- Developed a PostgreSQL-backed application using Prisma for data modeling, persistence, and structured access to user/session data.
- Implemented JWT-based authentication to protect API endpoints and reduce unauthorized access or endpoint abuse.
- Applied AI evaluation concepts to assess clarity, tone, confidence, relevance, and effectiveness of user responses in simulated conversations.
- Used Stable Diffusion to generate 10,000+ synthetic character images for interactive dialogue scenarios and simulated conversation practice.