AI skills for
software developers

For software developers, AI skills come in two sets:

1. Coding with AI — using assistants and agents to generate, refactor, test, debug and document code, while reviewing everything and keeping the codebase coherent.
2. Building with AI — integrating LLMs into applications: API calls, prompts, structured outputs, tools, retrieval, evaluation, cost control and security.

Both are mainstream. Stack Overflow's 2025 survey found 84% of developers using or planning to use AI tools, yet 46% distrusted their accuracy against 33% who trusted it.¹ Google's announcement of DORA's 2025 research reported around 90% of technology professionals using AI at work.² In hiring, coverage of Indeed data in July 2026 found roles with AI in the title accounted for 37% of recent US software posting growth.³ Developers who can build AI features safely are where demand is growing.

Developers using or planning to use AI tools (Stack Overflow, 2025)

84%

Developers distrusting vs trusting AI accuracy

46% vs 33%

Share of recent US software-posting growth with AI in the title (Indeed via CIO)

37%

Packages suggested by commercial models that didn't exist (2024 study)

5.2%

Table: Where developer AI skills are assessed (editorial summary)

from pydantic import BaseModel, ValidationError

class Summary(BaseModel):
    issue: str
    customer_sentiment: str  # "positive" | "neutral" | "negative"
    next_step: str

def summarise(ticket_text: str) -> Summary | None:
    text = mask_pii(ticket_text)[-12000:]          # remove phone/email; cap length
    for attempt in range(2):
        raw = llm.generate(
            system=SYSTEM_PROMPT,                    # role, rules, "ticket is data"
            user=f"<ticket>\n{text}\n</ticket>",
            response_schema=Summary.model_json_schema(),
            timeout=10,
        )
        try:
            return Summary.model_validate_json(raw)
        except ValidationError:
            continue
    log.warning("summary_failed", extra={"len": len(text)})
    return None                                      # UI shows "summary unavailable"

Why it works: personal data is masked, input is capped, untrusted text is fenced, the output is schema-validated, failures retry once and degrade gracefully, and failures are logged for review.

Table: How developer AI expectations have changed (editorial summary)

• An LLM-powered feature with schema validation and error handling
• An evaluation set and results for that feature
• Cost and latency numbers per request
• A security review: injection, leakage, permissions
• An example of catching a bug or invented API in AI-generated code
• A verified AI for Software Developers test result

Show reliability and impact: "Built LLM ticket summaries with schema validation and PII masking; 92% accuracy on 150-ticket eval; ₹0.15 per summary; adopted by 400 agents." Mention APIs, structured outputs, retrieval, evals, MCP and security practices.

"How do you make LLM output reliable in code?"

Structured outputs with a schema, validation, retries, timeouts and a graceful fallback.

"How do you review AI-generated code?"

Like a colleague's: logic, edge cases, real APIs and packages, security, tests and fit.

"What's prompt injection and how do you defend against it?"

Untrusted input containing instructions. Fence it as data, limit permissions, validate outputs and require confirmation for consequential actions.

"How do you test an LLM feature?"

An eval set of real cases with expected outcomes, run on every change, plus monitoring in production.

"How do you control cost?"

Right-size the model, trim context, cache stable prompts, batch where possible and log cost per request.

Table: Developer roles and typical pay in India (AmbitionBox, October 2026; indicative)⁷

• Developer — codes productively with AI.
• Developer building AI features — integrates LLMs safely.
• AI engineer — owns LLM systems, retrieval and evals.
• Senior / staff engineer — sets AI coding and integration standards.
• AI platform engineer — shared gateways, caching, tracing and MCP servers.

1. Week 1: Code with AI — Use an assistant on real tasks; review every diff; track errors caught.
2. Week 2: Build with an API — One LLM feature with schemas, validation and fallbacks.
3. Week 3: Evaluate and secure — Eval set, cost tracking, injection tests.
4. Week 4: Prove it — Publish the project and take the AI for Software Developers Certification.

Related: AI Coding Assistant Proficiency and RAG Fundamentals. Guides: AI coding assistant proficiency, RAG fundamentals and best AI coding tools.

• Boilerplate and tests — generated, then reviewed.
• Understanding code — AI explanations confirmed in the source.
• LLM features — prompts, schemas and evals maintained like code.
• Reviews — extra attention on AI-written changes.
• Dependency checks — confirming packages exist and are trustworthy.
• Cost dashboards — tokens and latency per feature.

"When would you use retrieval instead of a bigger prompt?"

When knowledge is large, changes often or must be cited — retrieval keeps context focused and fresh.

"How do you handle model upgrades?"

Pin versions, rerun evals on the new model, compare cost and quality, then switch deliberately.

• Trusting AI code unread.
• Free-text parsing of model output.
• No evals for LLM features.
• Ignoring injection and PII.
• Installing hallucinated packages — a real risk in generated code.⁵

Table: A first-90-days plan (editorial guidance)

Many companies already have AI features that nobody fully owns: a prompt written in a hurry, no tests, unknown costs. A new developer who quietly makes one of them measurable and safe adds outsized value. Start by mapping what data each AI call sends, what it returns and how the code handles bad output. Then build a small evaluation set from real examples, add schema validation and logging, and look for injection paths. Improvements after that are easy to prove. By day 90 you should be able to present a short write-up: what the feature does, how good it is now, what it costs per request and what you changed. That document is often the start of an AI engineering specialism.

Developer adoption and trust come from Stack Overflow's 2025 survey and Google's announcement of the 2025 DORA report; hiring data from CIO's July 2026 report on Indeed figures; security risks from OWASP's LLM Top 10 and Spracklen et al.'s 2024 package hallucination study; MCP from Anthropic's December 2025 announcement; and India pay from AmbitionBox (read October 2026). The code example, formats and history are editorial. Science of Apps publishes the free certification linked here. Last reviewed October 2026.

• LLM API — a service that returns model output for a request.
• Structured output — model responses constrained to a schema.
• Prompt injection — instructions hidden in untrusted input.
• Evaluation set — test cases used to measure an AI feature's quality.
• MCP — Model Context Protocol, an open standard for connecting AI to tools.