This content originally appeared on DEV Community and was authored by Jamie Cole
18 months of building with LLMs. Here's what survived my actual workflow.
The Stack
Not the trendy stuff. The tools I reach for every day.
Claude Pro — $20/mo
Core LLM. I use the API for automation and claude.ai for exploration. The 200K context window is genuinely class-leading.
Why it wins: Context. I can give it an entire codebase and say "what does this do?" and get a coherent answer.
Cursor — $20/mo
VS Code fork with AI baked in at the core. Not a plugin — an IDE designed around AI.
Why it wins: Codebase awareness. It indexes your project and actually understands context across files.
Pydantic — Free
For structured output from LLMs. Define your schema once, get validated output.
from pydantic import BaseModel
class WeatherResponse(BaseModel):
city: str
temp_c: float
condition: str
result = llm.parse_pydantic(user_prompt, WeatherResponse)
No more manual JSON parsing and validation.
Helicone — Free tier
LLM observability. See what's being sent to your models, track costs, spot patterns in failures.
Why it wins: Doesn't slow down your code. Drop-in logging that actually tells you useful things.
What I Stopped Using
LangChain: Too much abstraction for what it gives you. Raw API calls + Pydantic = 90% of what LangChain provides without the complexity.
向量数据库 for everything: Everyone's reaching for vector DBs. Most of the time, a simple keyword search or relational DB is faster and more reliable.
Complex prompt chaining: If your workflow needs 5 LLM calls chained together, your architecture is probably wrong.
The Honest Take
LLM tooling has matured. The "best" tools are the boring ones that stay out of your way:
- Claude for intelligence
- Cursor for coding
- Pydantic for structure
- Helicone for observability
Everything else is context-dependent. These are what survived 18 months of real work.
Writing about what actually works, not what's trendy.
This content originally appeared on DEV Community and was authored by Jamie Cole
Jamie Cole | Sciencx (2026-03-23T12:40:29+00:00) The LLM Tooling Stack I Actually Use in 2026 (After 18 Months of Testing). Retrieved from https://www.scien.cx/2026/03/23/the-llm-tooling-stack-i-actually-use-in-2026-after-18-months-of-testing/
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