AI Coding
AI coding on Alibaba Cloud centers on assistant tools that generate, complete, explain, and refactor code within an IDE or through APIs. Candidates should understand the workflow: the assistant receives context (open files, cursor position, instructions), proposes code, and the developer reviews and accepts it. Know that context quality drives output quality, so selecting relevant files and writing clear instructions improves results. Understand common capabilities: inline completion, chat-based explanation, unit test generation, and code translation between languages.
Key concepts include prompt specificity, iterative refinement, and the review step. AI-generated code must be reviewed for correctness, security, and license concerns; it is a productivity aid, not an authority. Understand that assistants may hallucinate APIs or packages, so verification against documentation and tests is required.
Traps: questions may imply AI coding tools guarantee bug-free output or can replace testing. They cannot. Another trap is assuming the tool sees the whole repository automatically; context is limited and must be curated. Watch for security framing: never paste secrets or credentials into prompts, and treat generated code as untrusted until reviewed.
Rule of thumb: treat the assistant as a fast junior developer. Give precise instructions, provide the relevant context, and always review and test the result before merging.
Worked example: a developer asks for a function to parse a config file. A vague prompt yields a plausible but wrong library call. Refining the prompt with the file format, language version, and error-handling expectations, plus running unit tests, produces correct, verified code.
Multimodal and Speech Services
This domain covers services that process images, audio, and video alongside text. Know the multimodal building blocks: vision-language models that accept images and text and return text (captioning, visual question answering, document extraction), image generation models that turn text prompts into images, and speech services for automatic speech recognition (ASR, speech to text) and text-to-speech (TTS, text to audio). Understand that these are distinct services with distinct input and output types, and that pipelines chain them: for example, ASR transcribes audio, then a language model summarizes the transcript.
Key concepts include prompt design for images (describing style, composition, and constraints), audio formats and sampling for speech, and latency differences: streaming ASR returns partial results for real-time use, while batch transcription suits recorded files. Know that TTS voices differ by timbre and language, and that quality depends on input text normalization.
Traps: questions may propose a text-only model for an image task, or use image generation for a task needing accurate text extraction (generation creates plausible images, not faithful transcription). Another trap is confusing ASR with TTS direction. Watch for real-time versus batch mismatches: streaming is needed for live captioning.
Rule of thumb: identify input and output modalities first, then choose the service whose signature matches. For faithful reading of an image, use a vision-language model, not an image generator.
Worked example: a meeting recorder must produce live captions and a written summary. Use streaming ASR for partial captions, then a language model over the final transcript for the summary.