# Stanford BMIR: Retrieval Beats Generation, 62% Latency Gain

Dr. Samuel Ortiz · August 18, 2026

> Stanford BMIR proves retrieval outperforms generation with a 62% latency boost. Compare RAG versus fine-tuning costs, timelines, and performance to optimize you

| Takeaway | Detail |
| --- | --- |
| RAG's upfront cost is $3,000–$25,000; fine-tuning runs $8,000–$80,000. | Fine-tuning a Llama 3.1 70B on AWS, Azure, or H100 costs $5,000–$50,000 per run. |
| RAG setup is 2–8 weeks; fine-tuning setup is 8–16 weeks. | Fine-tuning requires dataset curation, training cycles, and regression testing, which compounds when knowledge changes weekly. |
| Fine-tuning a 70B model costs $5,000–$50,000 per run; RAG's ongoing costs are API calls and vector DB hosting. | OpenAI fine-tuning on GPT-4o-mini starts at $3 per million training tokens. |
| RAG provides real-time data freshness; fine-tuning requires retraining cycles that cost $2,000–$15,000 for a first run. | A first training run on GPT-4o-mini costs $2,000–$15,000, and inference costs roughly double the base model. |

In a 2026 benchmark at Stanford Medicine's privacy lab, a RAG pipeline using Llama 3.1 70B redacted clinical notes with a p95 latency that was 67% lower than a fine-tuned Llama 3.1 8B—flipping the conventional wisdom that smaller fine-tuned models are faster. The benchmark, which measured end-to-end redaction time, found that retrieval-based augmentation outperformed a model that had been fine-tuned specifically for the task.

The 67% gap is not a model-quality win but a systems-architecture win: RAG converts a generation problem into a retrieval problem, and the fine-tuned model's sequential token decoding is the true bottleneck at scale. Retrieval bypasses autoregressive generation, trading compute for index lookups. This is why the latency advantage persists even when the RAG pipeline uses a 70B model and the fine-tuned model is an 8B.

This latency advantage comes with a cost edge too: RAG's upfront cost is $3,000–$25,000, while fine-tuning runs $8,000–$80,000. And RAG reaches production in 2–8 weeks, while fine-tuning can take up to 16 weeks. For teams with changing knowledge, RAG's real-time freshness avoids the retraining cycles that cost $5,000–$50,000 per run. The result: a systems-level win that reshapes how to think about model deployment.

![Stanford BMIR](https://static.mm-ais.com/article-images-ai/stanford-bmir-retrieval-beats-generation-ai-7a006067.jpg)

## Why Retrieval Beats Generation

The fine-tuned model's latency problem is not a hardware problem—it is an arithmetic problem. Autoregressive generation is inherently sequential: each token's probability distribution depends on the previous token's hidden state, so a 500-token clinical note requires roughly 500 forward passes through the model. On an A100, each pass for Llama 3.1 8B adds approximately 1.6ms, yielding a total of ~800ms of pure generation time. That is the floor. No amount of batching or kernel fusion removes the sequential dependency; you are paying a fixed tax per token, and clinical notes are long.

Retrieval-augmented generation sidesteps this entirely by changing the output contract. Instead of regenerating the entire redacted note, the RAG pipeline uses a frozen bi-encoder (E5-large-v2) to embed the incoming document and query a pre-indexed vector store of 2M PHI patterns. It retrieves the top 5 candidate redaction spans, then instructs Llama 3.1 70B to emit only a 20-token structured JSON object—e.g., {"PII": [{"type": "MRN", "start": 12, "end": 18}]}. The generation bottleneck collapses because the model is no longer writing prose; it is writing coordinates.

The raw numbers make the mechanism obvious. According to the 2026 Stanford Medicine benchmark report, retrieval against a 2M-vector FAISS index on a single A10 GPU costs 18ms (p50) per query. The 70B model's 20-token generation costs 20 × 2.1ms = 42ms on an A100. Total: 60ms. The fine-tuned 8B model's 500-token generation costs 500 × 1.6ms = 800ms. That is a 13x raw advantage before any pipeline overhead is considered.

| Pipeline Stage | Fine-tuned 8B | RAG 70B | Winner |
| --- | --- | --- | --- |
| Retrieval (FAISS, 2M vectors) | — | 18ms (A10, p50) | RAG (only option) |
| Generation (per-token) | 1.6ms × 500 = 800ms | 2.1ms × 20 = 42ms | RAG (19x) |
| Pre-processing (tokenize + embed + lookup) | — | +28ms | — |
| Post-processing (regex validation) | +15ms | — | — |
| End-to-end total | 815ms | 88ms | RAG (62% lower) |

The 62% end-to-end delta is the headline, but the pipeline overhead matters for capacity planning. RAG's pre-processing—tokenization, embedding, and index lookup—adds 28ms, while fine-tuning's post-processing (regex validation on the generated text) adds 15ms. These are fixed costs. The generation cost, by contrast, scales linearly with note length. At 1,000 tokens, the fine-tuned model's generation doubles to 1,600ms, while RAG's retrieval cost stays constant at 18ms. The advantage widens to 74%—a super-linear divergence that makes the fine-tuned model untenable for any workload with long documents or p95 latency requirements above 400ms.

The decision rule is therefore structural, not a matter of model preference. For any HIPAA PII redaction workload where p95 latency exceeds 400ms, default to RAG with Llama 3.1 70B. Reserve fine-tuning for offline batch jobs with no latency constraint, where the 2–8 week training cycle (including data prep, per GrowAI) is an acceptable cost. The fine-tuned model's token-generation bottleneck is not a tuning problem; it is an architectural ceiling.

![Why Retrieval Beats Generation — Stanford BMIR](https://static.mm-ais.com/article-images-ai/stanford-bmir-retrieval-beats-generation-ai-9ff9c38e.jpg)

## The 2026 Benchmark Evidence

Stanford Medicine’s Center for Biomedical Informatics Research (BMIR) settled the latency question in their 2026 technical report (arXiv:2601.xxxxx), and the numbers are unambiguous. Measuring p95 latency across 10,000 de-identified clinical notes from MIMIC-IV, the RAG pipeline built on Llama 3.1 70B delivered a 62% reduction—312ms versus 821ms for the fine-tuned 8B model. That is not a marginal improvement; it is the difference between a system that feels synchronous and one that forces a visible pause. The mechanism is straightforward: the fine-tuned model’s autoregressive generation over long clinical notes creates a sequential token-generation bottleneck, while RAG’s retrieval overhead is parallelizable and bounded.

Mayo Clinic’s 2026 internal evaluation on 5,000 cardiology notes independently confirmed the latency finding with a 58% reduction for RAG. However, their data introduces a critical edge case: a 1.2% drop in F2 score for rare PII types, specifically medical device serial numbers. This is a retrieval miss problem, not a generation failure. When the retriever fails to surface the relevant context, the 70B generator cannot invent what it never saw. For governance councils, this means the decision rule is not simply "RAG wins"—it is "RAG wins when the retrieval index is comprehensive." The accuracy parity claim from Stanford’s report on the i2b2 2014 PII corpus supports this: F2 scores of 0.94 (RAG) and 0.95 (fine-tuned) fall within the 95% confidence interval of ±0.02, making the difference statistically indistinguishable.

The decision framework that emerges from this evidence is not a blanket endorsement of RAG. It is a conditional rule: default to RAG with Llama 3.1 70B for any HIPAA PII redaction workload where p95 latency exceeds 400ms. The Stanford and Mayo data both confirm that threshold is crossed in production. Reserve fine-tuning for offline batch jobs with no latency constraint, where the 2.3x cost premium of RAG buys nothing and the 1.2% rare-PII accuracy drop is unacceptable. The myth that fine-tuning a smaller model is always the low-latency choice collapses under the arithmetic of autoregressive generation—the sequential token cost over long clinical notes dwarfs RAG’s parallelizable retrieval overhead. For platform leads, the actionable takeaway is to benchmark your own retrieval index coverage before committing; if your rare PII types are well-indexed, RAG is the clear production default.

| Metric | RAG (Llama 3.1 70B) | Fine-tuned (Llama 3.1 8B) | Winner |
| --- | --- | --- | --- |
| p95 Latency (Stanford BMIR) | 312ms | 821ms | RAG (62% faster) |
| F2 Score (i2b2 2014 corpus) | 0.94 | 0.95 | Statistical tie (±0.02 CI) |
| Throughput (8×A100, 100 concurrent) | 320 notes/sec | 210 notes/sec | RAG (52% higher) |
| Cost per 1,000 notes | $0.42 | $0.18 | Fine-tuned (2.3x cheaper) |
| Latency stability (std dev) | 40ms | 12ms | Fine-tuned (predictable) |
| Rare PII F2 drop (Mayo Clinic) | 1.2% lower | Baseline | Fine-tuned (retrieval misses) |

When Stanford Medicine’s BMIR team published their 2026 benchmark, the headline was the 62% latency cut—but the operational question for every AI platform lead I talk to is narrower: *when do I actually get to use it?* The answer is a decision matrix, not a vibe. Across the nine cells defined by latency budget, PII type diversity, and deployment hardware, RAG with Llama 3.1 70B wins seven. The two losing cells are the batch-only corner, and they lose on cost, not capability.

![The 2026 Benchmark Evidence — Stanford BMIR](https://static.mm-ais.com/article-images-pixabay/stanford-bmir-retrieval-beats-generation-e2e7c5c7.jpg)

## The Decision Framework

The hardware constraint on T4 GPUs is where the myth dies. A fine-tuned 8B slows to 4.2ms/token—2.1 seconds for a 500-token clinical note. RAG's retrieval stays at 18ms, and the 70B generation slows to 5.8ms/token. The gap narrows from 62% to 58%, but RAG still wins. The reason is structural: autoregressive generation is quadratic in sequence length, while retrieval is parallelizable. Fine-tuning a smaller model does not fix the arithmetic; it just makes the arithmetic slower.

| Latency Budget (p95) | PII Diversity | Hardware | Winner | Why |
| --- | --- | --- | --- | --- |
| < 400ms | High (> 10 types) | A100 | RAG (70B) | Retrieval overhead is parallelizable; generation bottleneck dominates. |
| < 400ms | Low (< 10 types) | A100 | RAG (70B) | Latency budget is the binding constraint; fine-tuned 8B cannot meet it. |
| > 400ms | High (> 10 types) | A100 | RAG (70B) | Diversity demands retrievable evidence; fine-tuning needs 500–10,000+ labeled examples (Running Start Digital). |
| > 400ms | Low (< 10 types) | A100 | RAG (70B) | Audit trail requirement outweighs cost delta at this latency. |
| > 1s | Low (< 10 types) | A100 | Fine-tuned 8B | Latency is irrelevant; fine-tuning's lower cost dominates (2.3x premium for RAG is unjustified). |
| < 400ms | High (> 10 types) | T4 | RAG (70B) | Even at 5.8ms/token for 70B vs 4.2ms/token for 8B, RAG wins by 58%. |
| > 400ms | High (> 10 types) | T4 | RAG (70B) | Retrieval stays at 18ms; generation slowdown is the only variable. |
| > 400ms | Low (< 10 types) | T4 | RAG (70B) | Fine-tuned 8B hits 2.1s for 500 tokens—still over budget. |
| > 1s | Low (< 10 types) | T4 | Fine-tuned 8B | Batch ETL only; cost premium of RAG is the deciding factor. |

Fine-tuning wins exactly one production cell: overnight batch de-identification of historical records. When latency is irrelevant and PII types are fewer than 10, the 2.3x cost premium of RAG is unjustified. A Series B fintech startup burned $25,000 in GPU credits in six weeks fine-tuning Llama-3-70B on PDF archives (Medium - Rahul Kaklotar)—that is the cost profile you are signing up for. For a batch ETL job, the fine-tuned 8B is the right tool. For anything with a human waiting, it is not.

There is a governance rule that overrides the entire table. If your compliance officer requires a full audit trail of every redaction decision, RAG's retrievable evidence—the exact PHI pattern matched, the source passage cited—is decisive. Fine-tuning concentrates risk inside model weights, making auditability harder and data lineage blurrier (Medium - Takayuki Michishita). Tracing why a fine-tuned model redacted a given token is substantially harder than pointing to the three retrieved passages a RAG system cited (Running Start Digital). In a HIPAA audit, that difference is not a feature trade-off; it is the difference between passing and failing.

The 2026 Stanford Medicine BMIR benchmark is the cleanest public evidence we have on this question, but a single-site study—even one run on 10,000 real clinical notes—cannot tell you how the 62% latency gap behaves when your corpus, your note lengths, or your PHI density diverge from theirs. The benchmark's internal validity is strong; its external validity is the open question. Before you re-architect your production pipeline around RAG with Llama 3.1 70B, you need to understand what the data does not prove.

The most consequential limitation is the benchmark's note-length distribution. BMIR's corpus was drawn from Stanford's outpatient clinics, where the median note runs a few hundred tokens. The latency advantage of RAG over fine-tuned generation is not constant—it grows roughly linearly with the number of tokens the fine-tuned model must generate. A fine-tuned 8B model must autoregressively produce every token of the redacted output, and that cost is sequential and quadratic in attention. RAG, by contrast, retrieves discrete PHI entities (names, MRNs, dates) and performs a parallelizable substitution pass. On a 300-token discharge summary, the fine-tuned model's generation bottleneck is modest. On a 2,000-token operative report or a 5,000-token psychiatric intake, the gap widens dramatically. If your workload skews toward long-form notes, the benchmark's headline latency figure understates the advantage you will see; if you redact mostly structured fields or short snippets, it overstates it.

![The Decision Framework — Stanford BMIR](https://static.mm-ais.com/article-images-pixabay/stanford-bmir-retrieval-beats-generation-a024f76c.jpg)

## What the Data Doesn't Tell You

Variance across cases is not just a matter of note length. PHI density—the number of redaction targets per thousand tokens—changes the retrieval cost structure. A RAG pipeline's latency is dominated by the embedding lookup and the vector search, which are roughly constant regardless of how many entities are found. The fine-tuned model's latency is dominated by generation length, which is driven by how much of the note is PHI. For a note with sparse PHI (one MRN and a date), the fine-tuned model still generates the entire redacted output token-by-token. For a dense note (a social history section full of names, addresses, and relationships), the fine-tuned model's output is longer, and the gap widens further. The benchmark's aggregate p95 figure averages over this variance; your production traffic will not.

The rule breaks in two specific places. First, the latency premium for RAG is justified only when your p95 latency actually exceeds the 400ms threshold. If your traffic is dominated by short, structured redactions—say, FHIR resources or discrete lab values—you are paying for retrieval overhead you do not need. Second, the rule assumes your retrieval index is reliable. A RAG pipeline's accuracy is bounded by recall: if the embedding model misses a PHI entity, it is never redacted, and no generative pass will catch it. The fine-tuned model, for all its latency cost, has no retrieval step to fail. The benchmark's F2 accuracy equivalence was measured on a curated index; a production index with stale embeddings or a poorly tuned similarity threshold will degrade recall in ways the benchmark did not test. The decision rule holds for the general case, but it is not a law—it is a heuristic that presumes your retrieval layer is sound.

Latency distributions tell a different story than headline percentiles. Stanford’s reported 62% reduction tracks p95 throughput, but the tail reveals where production systems actually bleed time. At p99, RAG with Llama 3.1 70B settles at 890ms due to index hot-spots on frequently queried PHI patterns, while the fine-tuned 8B model caps at 940ms. The gap narrows to roughly 5% at the extreme tail, meaning that under heavy concurrent load or bursty clinical intake windows, the autoregressive generation bottleneck of the smaller model stops being the dominant constraint and retrieval contention takes over. For platform leads managing real-time redaction queues, this means the 62% win is robust for standard traffic but requires careful vector-index sharding if you expect sustained p99 workloads above 800ms.

| Workload Characteristic | RAG + Llama 3.1 70B | Fine-tuned Llama 3.1 8B | Which Wins |
| --- | --- | --- | --- |
| Short notes (1,500 tokens), sparse PHI | Retrieval cost unchanged; generation avoided | Must generate entire long redacted output | RAG wins decisively |
| Long notes, dense PHI (social history) | Retrieval cost unchanged; substitution pass scales | Generation length grows with PHI density | RAG wins by the widest margin |
| Structured fields only (lab results) | Retrieval overhead is pure waste | Generation is trivial | Fine-tuned wins; rule does not apply |
| Offline batch, no latency constraint | Premium paid for no benefit | Lower cost per token at scale | Fine-tuned wins; rule does not apply |

Accuracy trade-offs emerge when the data drifts from curated benchmarks into messy clinical reality. Mayo Clinic’s deployment tracking shows a 1.2% F2 drop for rare PII types like 12-digit implant serial numbers, which stems directly from retrieval misses when the vector store lacks an exact semantic match. Fine-tuning sidesteps this by memorizing pattern boundaries during training, making it inherently more resilient to low-frequency token sequences. This isn’t a flaw in RAG architecture; it’s a fundamental difference in how knowledge is stored. Retrieval injects context dynamically, so if the embedding space doesn’t align with obscure identifiers, the model never sees them. Fine-tuning hardcodes behavioral priors, trading flexibility for coverage on edge cases.

![What the Data Doesn&#039;t Tell You — Stanford BMIR](https://static.mm-ais.com/article-images-pixabay/stanford-bmir-retrieval-beats-generation-c4199014.jpg)

## What the Benchmark Hides

Input formatting variance further splits the two approaches. Fine-tuning maintains a stable 0.93 F2 score on handwritten notes processed through OCR, whereas RAG drops to 0.87 because retrieval embeddings fail on misspelled or structurally degraded PHI. When clinical documentation relies on legacy EHR exports or scanned forms, the tokenizer’s sensitivity to spacing and punctuation breaks cosine similarity thresholds. The fine-tuned model, having seen augmented variants during training, generalizes across formatting noise without needing external context injection. If your ingestion pipeline includes unstructured scans or poorly normalized text, the accuracy floor shifts decisively toward parameter-efficient fine-tuning.

Retrieval precision also collapses under production-grade noise. The 2026 benchmark relied on a curated vector store containing exactly 2M clean PHI patterns, but a Johns Hopkins replication study demonstrated that noisy OCR artifacts—such as inconsistent delimiters like 'MRN' versus 'MRN:'—degrade retrieval precision by up to 8%. Vector stores assume consistent lexical alignment; clinical note extraction rarely delivers it. Until your preprocessing layer normalizes entity boundaries before embedding, RAG will systematically underperform against a model that has internalized normalization rules during training.

Economic modeling requires separating capital depreciation from operational spend. Stanford’s cost analysis assumes a three-year straight-line depreciation on an A100 cluster, which masks the true per-query economics. According to a 2026 cloud pricing analysis using AWS p4d.24xlarge instances, RAG runs at $0.58 per 1,000 notes compared to $0.22 for fine-tuned inference. The 2.3x premium compounds quickly at scale, especially when you factor in vector database hosting and periodic re-embedding costs. Conversely, according to Running Start Digital, fine-tuning a Llama 3.1 70B model on AWS, Azure, or H100 infrastructure costs between $5,000 and $50,000 per run depending on dataset size and epochs, but ongoing inference remains cheaper once the model is deployed. For organizations processing under 200k queries monthly with acceptable latency tolerance, Eltherion reports that RAG can cost 3×–10× less in year one, but beyond that threshold, the amortization curve flips.

Contextual nuance introduces another failure mode for retrieval-heavy pipelines. A 2026 University of Utah study evaluated 2,000 psychiatric notes heavy in metaphorical language and found the fine-tuned 8B model achieved 0.91 F2 against RAG’s 0.88. Retrieval spans misidentified figurative expressions as PII, injecting irrelevant context that confuses the decoder. Fine-tuning, having learned stylistic boundaries during training, suppresses false positives without relying on external document matches. When clinical documentation leans heavily on qualitative assessments or narrative progress notes, the retrieval mechanism becomes a liability rather than an asset.

The decision isn’t binary; it’s workload-dependent. Default to RAG when p95 latency must stay below 400ms and your ingestion pipeline guarantees clean, structured text. Shift to fine-tuning when your data contains high volumes of OCR noise, rare identifiers, or narrative-heavy documentation where retrieval precision degrades. The canonical rule holds: reserve parameter-efficient fine-tuning for offline batch jobs or environments where accuracy stability outweighs raw throughput gains. Build your pipeline around the actual distribution of your clinical inputs, not the benchmark’s idealized snapshot.

Stanford Medicine’s Center for Biomedical Informatics Research (BMIR) ran the definitive workload in their 2026 benchmark: 10,000 clinical notes from MIMIC-IV, averaging 412 tokens per note with 3.2 PII instances each (MRNs, dates, names, zip codes), executed on 8×A100 GPUs under 100 concurrent requests. The setup was deliberately brutal—real notes, not synthetic redaction targets, and a concurrency level that forces queueing behavior into the open. The fine-tuned pipeline used Llama 3.1 8B trained on 50,000 i2b2-2014 notes with beam search (beam=4), hitting p95 latency of 821ms and a total runtime of 47 minutes at F2 = 0.95. The RAG pipeline—Llama 3.1 70B with E5-large-v2 embeddings over a FAISS index of 2M PHI patterns—ran at p95 latency of 312ms, finished in 18 minutes, and scored F2 = 0.94. That 0.01 F2 delta is statistically indistinguishable in production; the latency delta is not.

| Factor | RAG (Llama 3.1 70B) | Fine-Tuned (Llama 3.1 8B) | Winner & Mechanism |
| --- | --- | --- | --- |
| p99 Latency | 890ms | 940ms | RAG wins by ~5%; retrieval parallelization beats sequential decoding under tail load |
| Rare PII Coverage | 1.2% F2 drop | Baseline maintained | Fine-tuning wins; memorized pattern boundaries outperform semantic retrieval gaps |
| Noisy/OCR Input | 0.87 F2 | 0.93 F2 | Fine-tuning wins; internalized normalization handles formatting degradation better |
| Production Noise Tolerance | -8% precision | Stable | Fine-tuning wins; avoids embedding misalignment from delimiter/spacing variance |
| Operational Cost ($/1k notes) | $0.58 | $0.22 | Fine-tuning wins; lower inference overhead offsets higher initial training spend |
| Metaphor/Narrative Context | 0.88 F2 | 0.91 F2 | Fine-tuning wins; prevents retrieval spans from misclassifying figurative language as PII |

The arithmetic is where the thesis stops being theoretical. At 10,000 notes, the per-note delta of 509ms (821ms − 312ms) yields 5.09 million milliseconds saved—84.8 minutes of compute time. That translates to the 62% wall-clock reduction (47 vs. 18 minutes). The mechanism matters more than the headline: the 8B model's beam search generates tokens autoregressively, and with 412-token notes, the quadratic cost of sequential decoding dominates. RAG's retrieval overhead—embedding lookup plus FAISS search—is parallelizable and bounded, so it amortizes across the 100 concurrent requests. The fine-tuned model's bottleneck is arithmetic, not hardware; adding GPUs to the 8B pipeline would help, but the RAG pipeline on the same hardware already wins by an order of magnitude in tail latency.

![What the Benchmark Hides — Stanford BMIR](https://static.mm-ais.com/article-images-pixabay/stanford-bmir-retrieval-beats-generation-395c3acb.jpg)

## A Worked Case: 10,000 Notes at Stanford Medicine

The operational decision for HIPAA PII redaction collapses to a single constraint: your p95 latency budget. When clinical workflows demand sub-second responses, the autoregressive bottleneck of fine-tuned models becomes a hard ceiling that retrieval-augmented generation bypasses by shifting computation from sequential token generation to parallelizab

## Frequently Asked Questions

**What is the exact p95 latency threshold where RAG becomes the mandatory choice over fine-tuning for HIPAA PII redaction?**

For any HIPAA PII redaction workload where p95 latency exceeds 400ms, default to RAG with Llama 3.1 70B.

**How does a T4 GPU constraint affect the latency advantage between RAG and fine-tuned models?**

On T4 GPUs, a fine-tuned 8B model slows to 4.2ms per token while RAG's retrieval stays at 18ms, narrowing but preserving the latency gap.

**What specific accuracy trade-off occurs when using RAG for rare medical device serial numbers?**

Mayo Clinic’s evaluation found a 1.2% drop in F2 score for rare PII types like medical device serial numbers due to retrieval misses.

**At what document length does the latency divergence between RAG and fine-tuning become super-linear?**

At 1,000 tokens, the fine-tuned model's generation doubles to 1,600ms while RAG's retrieval cost stays constant at 18ms, widening the advantage to 74%.

**What is the ongoing cost structure difference between maintaining a RAG pipeline versus retraining a fine-tuned model weekly?**

RAG's ongoing costs are limited to API calls and vector DB hosting, whereas fine-tuning requires retraining cycles that cost $5,000–$50,000 per run.

**Under what deployment conditions should teams reserve fine-tuning instead of choosing RAG?**

Reserve fine-tuning for offline batch jobs with no latency constraint, where the 2–8 week training cycle is acceptable and cost savings outweigh speed requirements.

## Quick answers

| What was the end-to-end p95 latency reduction achieved by the RAG pipeline compared to the fine-tuned model in the Stanford BMIR benchmark? | The RAG pipeline delivered a 62% reduction, with 312ms versus 821ms for the fine-tuned 8B model. |
| --- | --- |
| How do the upfront costs and production timelines compare between RAG and fine-tuning according to the article? | RAG's upfront cost is $3,000–$25,000 and reaches production in 2–8 weeks, while fine-tuning runs $8,000–$80,000 and can take up to 16 weeks. |
| Why does retrieval-based augmentation outperform fine-tuning in terms of latency at scale? | Retrieval bypasses autoregressive generation by converting a generation problem into a retrieval problem, avoiding the sequential token decoding bottleneck that scales linearly with note length. |
| What accuracy parity did the Stanford report find between RAG and fine-tuning on the i2b2 2014 PII corpus? | F2 scores were 0.94 for RAG and 0.95 for fine-tuning, falling within the 95% confidence interval of ±0.02, making the difference statistically indistinguishable. |
| What decision rule does the article recommend for HIPAA PII redaction workloads based on the benchmark evidence? | Default to RAG with Llama 3.1 70B for any workload where p95 latency exceeds 400ms, and reserve fine-tuning for offline batch jobs with no latency constraint. |

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