Thirty days of search, agents, structured output, dedup, caching, and full retrieval-augmented generation. The function you wrote on Day 28 takes a list of queries and returns a structured, confidence-rated research report. Compare that to Day 3, when len(results) felt like new territory.
The capstone felt like assembly, not invention. Every piece — search loops, dedup sets, Pydantic models, Literal labels, model_dump — was already in my head. The function just stitched them together.
That shift — from "each concept is new" to "these are pieces I compose" — is the goal. Which pattern will you reach for most in your own work?
Probably the Pydantic-plus-overwrite shape. Let the agent produce the prose fields, let my code own the URLs and IDs. Now I can't imagine writing a RAG function any other way — the grounded provenance makes such a difference.
That habit — intelligence where it helps, ground truth where it matters — transfers everywhere. The next track, ai-for-makers, is where agents start remembering between calls. Your functions stop being stateless one-shots and become stateful conversations. Rate yourself now; compare with Day 1.
A retrieval-augmented toolkit — 20 functions across four weeks:
Week 1 — search primitives: search_count_results, search_first_snippet, extract_all_urls, format_search_results, search_summary_dict
Week 2 — search + agent: summarize_top_result, classify_result_relevance, batch_summarize_results, extract_fact_from_search, classify_all_results
Week 3 — retrieval upgrades: score_result_semantic_match, rank_results_by_agent_depth, cache_search_queries, deduplicate_results_by_domain, compare_two_queries
Week 4 — full RAG: retrieve_and_answer, structured_research_brief, multi_query_brief, confidence_rated_answer, research_assistant
The patterns you now own: search loops, list comprehensions over dicts, Agent(model).run_sync(prompt), result_type=Literal[...], Pydantic BaseModel extraction, dict caches, set-based dedup, fan-out retrieval, and structured RAG with grounded sources. Next in the makers track, agents start remembering — stateful, multi-turn, memory-augmented. Same Agent(model), bigger context.
Thirty days of search, agents, structured output, dedup, caching, and full retrieval-augmented generation. The function you wrote on Day 28 takes a list of queries and returns a structured, confidence-rated research report. Compare that to Day 3, when len(results) felt like new territory.
The capstone felt like assembly, not invention. Every piece — search loops, dedup sets, Pydantic models, Literal labels, model_dump — was already in my head. The function just stitched them together.
That shift — from "each concept is new" to "these are pieces I compose" — is the goal. Which pattern will you reach for most in your own work?
Probably the Pydantic-plus-overwrite shape. Let the agent produce the prose fields, let my code own the URLs and IDs. Now I can't imagine writing a RAG function any other way — the grounded provenance makes such a difference.
That habit — intelligence where it helps, ground truth where it matters — transfers everywhere. The next track, ai-for-makers, is where agents start remembering between calls. Your functions stop being stateless one-shots and become stateful conversations. Rate yourself now; compare with Day 1.
A retrieval-augmented toolkit — 20 functions across four weeks:
Week 1 — search primitives: search_count_results, search_first_snippet, extract_all_urls, format_search_results, search_summary_dict
Week 2 — search + agent: summarize_top_result, classify_result_relevance, batch_summarize_results, extract_fact_from_search, classify_all_results
Week 3 — retrieval upgrades: score_result_semantic_match, rank_results_by_agent_depth, cache_search_queries, deduplicate_results_by_domain, compare_two_queries
Week 4 — full RAG: retrieve_and_answer, structured_research_brief, multi_query_brief, confidence_rated_answer, research_assistant
The patterns you now own: search loops, list comprehensions over dicts, Agent(model).run_sync(prompt), result_type=Literal[...], Pydantic BaseModel extraction, dict caches, set-based dedup, fan-out retrieval, and structured RAG with grounded sources. Next in the makers track, agents start remembering — stateful, multi-turn, memory-augmented. Same Agent(model), bigger context.
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