LLM application development
Build voice or vision LLM features
Speech-to-text, TTS, image understanding, document OCR pipelines with multimodal models.
~15 focused hours·intermediate
Tools: Whisper / OpenAI speech-to-text, Text-to-speech (OpenAI/ElevenLabs), Claude/GPT-4o vision, Gemini multimodal, OCR (Tesseract or model-based)
Market relevance — share of job ads asking for this
What employers mean
You should be able to…
- Transcribe audio (calls, voice notes) to text using a speech-to-text API
- Generate spoken responses (TTS) for a voice assistant
- Extract structured info from images (receipts, ID cards, screenshots) using vision models
- Build an OCR pipeline for scanned/handwritten documents, including regional-language forms
- Combine voice + text + image in a single multi-turn multimodal conversation
- Handle non-English/code-mixed audio (Hindi, Hinglish) in transcription pipelines
- Optimize multimodal calls for cost — downsample images, chunk long audio
Needs first: Integrate LLM APIs into an application
Learn — free, link-checked
The few resources that matter
Read · beginner · 20 min · docs.anthropic.com
PDF Support
Covers sending PDFs directly to a multimodal model as an alternative to a separate OCR/parsing step. — Anthropic
Read · beginner · 20 min · ai.google.dev
Image Understanding
A third vision API's approach — useful for comparing cost/quality across providers on the same image task. — Google
Read · beginner · 25 min · platform.openai.com
Vision
Shows how to send images to a vision-capable model and parse the response, the core of any document/photo pipeline. — OpenAI
Read · beginner · 25 min · docs.anthropic.com
Vision
Claude's vision docs cover image sizing/cost tradeoffs that matter once you're processing real scanned documents. — Anthropic
Read · beginner · 25 min · platform.openai.com
Speech to Text (Whisper)
Official guide to transcription, the standard first step for any voice-note or call-summarization pipeline. — OpenAI
Build from · intermediate · 90 min · github.com
OpenAI Cookbook
Battle-tested example notebooks (chat apps, vision, retries) you can lift directly into a real project. — OpenAI
Build from · intermediate · 90 min · github.com
Anthropic Cookbook
Official recipes including contextual retrieval and RAG evaluation patterns tuned specifically for Claude. — Anthropic
Practice
Voice-note-to-structured-ticket pipeline for Hindi support calls
Build a pipeline that takes a short Hindi/Hinglish voice note (recorded or generated audio), transcribes it via a speech-to-text API, and uses an LLM to convert it into a structured support ticket (category, urgency, English summary). Add a vision step that extracts info from an attached photo, such as a product or damage photo, into the same ticket.
Done when
- Pipeline correctly transcribes at least 10 Hindi/Hinglish audio samples with usable, not necessarily perfect, accuracy
- Transcript is converted into a structured ticket schema (category, urgency, English summary) via the LLM
- A photo input is processed by a vision model and its extracted details merged into the same ticket
- End-to-end pipeline runs on a new voice note + photo pair in under 30 seconds and is demoed in a README/video
Prove it
Evidence a recruiter can check
- Public GitHub repo with sample audio/image inputs (synthetic or self-recorded) and output tickets
- A demo video showing voice note in, structured ticket out
- Cost/latency numbers reported (per-minute audio, per-image) in the README
- Notes on transcription accuracy/failure cases for code-mixed speech
Interview
Questions you'll get asked
- How would you build a pipeline that transcribes a customer support call and summarizes it?
- What's the difference between using an OCR-specific model vs. a general vision-LLM for document extraction?
- How do you handle a scanned document that's rotated, low-resolution, or partially handwritten?
- How would you extract structured data from a photo of a physical receipt taken on a phone?
- What are the cost/latency tradeoffs of sending high-res images to a vision model, and how do you mitigate them?
- How would you build a voice assistant that works well with Hindi-English code-switching?