lesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaplesson 4 of 5 · rag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swaprag vs long context vs fine-tune — the decision that's killed more ai startups than any model swap
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Final drill. Invert the rubric. Given a product spec AND a fork someone picked, decide whether the fork fits the product.
Write evaluate_strategy(product, fork) that returns a tuple
(ok: bool, reason: str).
The product spec has the same fields as in step 08:
corpus_size_tokens, update_frequency_days, style_critical,
cost_per_call_max, latency_budget_ms.
The fork is one of: "rag", "long_context", "fine_tune",
"hybrid".
Apply these checks (first failure wins; if none fail, return ok=True):
- If fork is
"long_context"ANDcorpus_size_tokens > 2_000_000: return(False, "corpus too large for long-context window") - If fork is
"fine_tune"ANDupdate_frequency_days <= 7: return(False, "freshness too high for fine-tune") - If fork is
"rag"ANDcorpus_size_tokens <= 200_000ANDupdate_frequency_days >= 90: return(False, "RAG overkill for small stable corpus") - If fork is
"hybrid"AND NOTstyle_critical: return(False, "hybrid wasted without style demand") - Otherwise return
(True, "fork fits product")
Three product-fork pairs run. Expected output:
Harvey + fine_tune: (False, 'freshness too high for fine-tune')
Glean + long_context: (False, 'corpus too large for long-context window')
Handbook bot + rag: (False, 'RAG overkill for small stable corpus')