OpenAI API
Build · Test
Connect model calls, tool use and structured responses to your own application.
All capabilities · LLM application development
Build voice or vision LLM features
Speech-to-text, TTS, image understanding, document OCR pipelines with multimodal models.
Explore 4 tools for this projectMarket relevance
Which roles ask for this — and how often
Share of job postings in India, per role, that name this capability.
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
Tools for practice
Choose a tool for the job
Start with one tool for each part of your project. You don’t need to learn them all.
4 tools to explore
Claude API
Build · Test
Build model-backed features with messages, tool use and responses you can evaluate.
Gemini API
Build
Prototype features that combine text, images or other supported input types.
ElevenLabs
Voice
Voice your script and experiment with delivery, pacing and voice consistency across scenes.
Limited free plan
Monthly credits; commercial licensing is on paid plans.
Practice
Voice-note-to-structured-ticket pipeline for Hindi support calls
Take short Hindi and Hinglish clips from the Common Voice Hindi set, or record your own on a phone, transcribe them with a speech-to-text API, and have an LLM turn each transcript into a structured support ticket: category, urgency, English summary. Add a vision step that reads a photo you take yourself - a damaged product, a printed bill - and merges what it extracts into the same ticket. Report cost and latency per minute of audio and per image, and catalogue where code-mixed speech breaks the transcription.
Start from
Milestones
- Get 10 Hindi clips transcribing end to end and read through the errors · ~3h
- Convert transcripts into the structured ticket schema · ~3.5h
- Add the vision step and merge photo details into the same ticket · ~3.5h
- Measure cost and latency per minute of audio and per image · ~2h
- Record the demo and write up the code-mixed failure cases · ~2h
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
- A demo recording: Hindi voice note and photo in, one structured ticket out, inside 30 seconds
- Ten transcripts next to the tickets they produced, so a reader can see where the model mis-heard and still got the ticket right
- Cost and latency per minute of audio and per image, measured rather than estimated
- A failure catalogue of the code-mixed Hindi-English phrases the transcriber consistently mangles
Signal it
Built a multimodal intake pipeline that turns a Hindi voice note and a product photo into one structured support ticket - speech-to-text and vision extraction merged into a single schema, with measured per-minute and per-image cost.
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?