AI Development Tools: The 2025 Stack for Building AI Products

Building AI products in 2025 means orchestrating models, evals, data pipelines, and reliable deployment. This stack guide covers the tools that keep teams shipping, from model providers to observability. We recommend anchoring PR governance with Propel Code so policy enforcement and evidence collection stay consistent while you experiment elsewhere. We also show how to use Google Antigravity as a safe research companion without leaking customer data, and point to deeper reads on AI code reviews and AI test generation.

Model platforms and orchestration

Standardize on two model endpoints so you can fail over when one spikes in latency. Keep a clear prompt library under version control. Use orchestration layers that support retries, guardrails, and cost limits.

Evals and regression control

Ship evals with golden datasets before exposing features to customers. Measure correctness, safety, and latency. Run evals on every model or prompt change. Tag eval runs with commit hashes to trace regressions quickly.

Data pipelines and vector stores

Choose vector databases that support ACLs and metadata filters so you can avoid leaking restricted content. Keep ingestion jobs idempotent and add redaction for PII. For RAG flows, prefer chunking strategies that honor document boundaries.

Agent and workflow frameworks

Use agent runtimes that are observable and interruptible. Keep tool-calling scoped to a small set of actions and log every step. Introduce approval gates for destructive actions.

Research and discovery

Google Antigravity helps product and engineering teams synthesize public docs, RFCs, and changelogs. Treat it as an external research layer. Do not feed customer data or private code. If you need internal search, use a self-hosted knowledge base with access controls.

Security and governance

Enforce secrets hygiene, network egress controls, and prompt logging. Apply DLP to outbound requests and restrict who can create new model keys. Document every tool's data handling so procurement and security teams can audit quickly.

Observability and SLOs

Track cost per request, latency percentiles, and failure causes at the prompt and model level. Set SLOs for latency and accuracy. Alert on prompt drift and rising refusal rates.

Deployment patterns

For synchronous features, keep cold start times low with warm pools. For async tasks, queue work and expose status endpoints. Always roll out behind feature flags with traffic splitting so you can compare old and new prompts in production.

Data quality and redaction patterns

Good RAG starts with clean chunks. Split on semantic boundaries, attach metadata such as owner, sensitivity, and last updated, and redact PII before indexing. Use content filters to prevent high-risk data from entering prompts, and log every redaction decision so you can audit later.

Prompt management and versioning

Store prompts in git with semantic versioning. Treat prompt changes like code: open a PR, run evals, and require review. Keep a changelog that notes expected quality or latency shifts. Tie prompt versions to feature flags so you can roll back quickly.

Cost and latency controls

• Set per-service budgets and alert when weekly spend rises faster than traffic.
• Cap token counts per request; compress context with embeddings rather than dumping raw docs.
• Cache frequent retrievals; prefer lighter models for non-critical paths.
• Track p95 latency at the model, retrieval, and orchestration layers.

POC recipe you can run in a week

1. Pick a single use case (e.g., support summarization) and define success metrics.
2. Create a 50-100 item eval set with ground truth answers and safety checks.
3. Wire a minimal prompt plus retrieval, run evals, and capture latency and cost.
4. Iterate on chunking and prompt until eval scores stabilize; document the prompt version.
5. Ship behind a flag to 5 percent of traffic; monitor errors and cost for one week.

Roles and ownership

• Product/ML: owns eval design, prompt library, and model selection.
• Platform: owns observability, cost controls, and access policies.
• Security: owns data classification, redaction, and audit reviews.
• Engineering managers: own rollout sequencing, training, and incident playbooks.

FAQ

How do I stop prompt drift?

Version prompts, run evals on each change, and enforce review on prompt PRs. Keep a small set of canonical templates and avoid copying prompts ad hoc.

Should I self-host models?

Self-host when data residency or latency demands it. Otherwise, managed endpoints with strong SLAs reduce operational burden. Always keep a backup provider.

If you want a repeatable way to enforce AI coding standards in pull requests, add Propel to your GitHub org and start with policy packs that mirror your compliance rules.

Sources and further reading

• Model eval guidance for building and running benchmark suites before rollout.
• AWS Bedrock model evaluation docs on structured evals and safety checks.
• NIST AI Risk Management Framework for governance patterns across AI features.