Every team building with LLMs faces the same question: how do we make this model work well for our specific use case? The base model knows a lot, but it does not know your product, your customers, your data, or your domain-specific terminology.
There are three primary strategies for customizing AI behavior: Prompt Engineering, Retrieval Augmented Generation (RAG), and Fine-Tuning. Each has dramatically different cost profiles, implementation timelines, and performance characteristics. Choosing the wrong strategy wastes weeks of engineering time and thousands of dollars. Choosing the right one can get you to production in days.
At CODERCOPS, we have shipped all three approaches across client projects. This guide is the decision framework we use internally -- the one we wish existed when we started building AI-powered products.
The Three Approaches at a Glance
Before diving deep, here is the summary:
| Factor | Prompt Engineering | RAG | Fine-Tuning |
|---|---|---|---|
| What it does | Instructs the model via system prompts and examples | Retrieves relevant context from your data at query time | Modifies the model's weights with your training data |
| Implementation time | Hours to days | 1-3 weeks | 3-8 weeks |
| Cost to implement | Near zero | $2K-$15K | $10K-$100K+ |
| Ongoing cost | Per-token API costs | API + vector DB + embeddings | API or hosting + periodic retraining |
| Data requirement | 5-20 examples | Your knowledge base (any size) | 500-10,000+ curated examples |
| Update frequency | Instant (change the prompt) | Real-time (update the index) | Days-weeks (retrain) |
| Best for | Tone, format, simple behavior | Knowledge bases, documents, dynamic data | Specialized behavior, domain expertise |
| Quality ceiling | Medium | High (for knowledge tasks) | Highest (for behavioral tasks) |
Prompt Engineering: The Starting Point
Prompt engineering is the practice of carefully crafting the instructions (system prompt), examples (few-shot), and context you provide to the model at inference time. It does not modify the model itself -- it modifies how you use it.
How It Works
┌──────────────────────────────────────┐
│ System Prompt │
│ "You are a customer support agent │
│ for Acme Corp. Be concise. │
│ Always reference the refund policy │
│ when relevant..." │
├──────────────────────────────────────┤
│ Few-Shot Examples │
│ User: "Can I get a refund?" │
│ Assistant: "Yes, Acme offers a │
│ 30-day refund. Here's how..." │
│ │
│ User: "My order is late" │
│ Assistant: "I'm sorry about that. │
│ Let me check the status..." │
├──────────────────────────────────────┤
│ User Query │
│ "I want to return my order" │
├──────────────────────────────────────┤
│ ▼ │
│ LLM Response │
│ "I can help with your return. Under │
│ Acme's 30-day policy..." │
└──────────────────────────────────────┘Everything happens in the context window. No additional infrastructure. No training.
When Prompt Engineering Is Enough
Prompt engineering is the right (and only needed) approach when:
- The model already has the knowledge. You are just shaping how it presents information it already knows.
- The customization is about behavior, not knowledge. Tone, format, response length, persona.
- Your data fits in the context window. If your entire knowledge base is under 50K tokens, you can include it directly in the prompt.
- You need to iterate fast. Prompt changes deploy instantly. No retraining, no reindexing.
Real Example: Venting Spot
One of our projects, Venting Spot, uses prompt engineering for its core matching feature. The system matches users who need to vent with compatible listeners based on personality traits and emotional state.
The matching logic is entirely prompt-engineered:
const MATCHING_PROMPT = `You are a compatibility matcher for a peer support platform.
Given two user profiles, evaluate their compatibility for a venting session.
MATCHING CRITERIA:
1. Emotional complementarity (high-energy venters pair well with calm listeners)
2. Topic alignment (similar experiences create empathy)
3. Communication style match (direct ↔ direct, gentle ↔ gentle)
4. Availability overlap
SCORING:
- Return a score from 0-100
- Scores above 70 are "good match"
- Scores above 85 are "excellent match"
- Include a brief explanation
IMPORTANT:
- Never match users who have flagged content in common negative patterns
- Prioritize emotional safety over all other factors
- When in doubt, do not match (false negatives are better than bad matches)
Return JSON: { "score": number, "explanation": string, "concerns": string[] }`;No RAG. No fine-tuning. Just a well-crafted prompt with clear criteria, scoring rubric, and safety constraints. It works because the model already understands human psychology and emotional dynamics -- we are directing that existing knowledge, not adding new knowledge.
Prompt Engineering Techniques
| Technique | Description | Example |
|---|---|---|
| System prompt | Set role, rules, constraints | "You are a medical triage assistant..." |
| Few-shot examples | Show desired input/output pairs | 3-5 representative examples |
| Chain-of-thought | Ask model to think step by step | "First analyze X, then evaluate Y..." |
| Output formatting | Specify exact output structure | "Return JSON with these fields..." |
| Negative constraints | Explicitly state what NOT to do | "Never mention competitor products" |
| Role-playing | Give the model a specific persona | "Respond as a senior tax advisor" |
| Temperature tuning | Adjust randomness (0 = deterministic) | temperature: 0.1 for factual tasks |
Limitations
- Context window ceiling. You can only include so much in a prompt. Once your reference data exceeds the context window (or even 30% of it, where performance starts degrading), you need RAG.
- No learning from data. The model does not learn from your historical data. It follows instructions but does not internalize patterns from thousands of examples.
- Prompt brittleness. Small changes to wording can cause large changes in behavior. Prompts need careful testing.
Cost Profile
| Item | Cost |
|---|---|
| Implementation | Engineer time only (hours to days) |
| Infrastructure | None (uses existing API) |
| Per-query cost | Standard API pricing |
| Maintenance | Minimal (update prompts as needed) |
| Total first-year cost (10K queries/day) | $500-$3,000/month (API costs only) |
RAG: Retrieval Augmented Generation
RAG is the strategy of dynamically retrieving relevant information from your data and injecting it into the model's context at query time. Instead of hoping the model knows the answer, you give it the answer (or the data needed to construct the answer) every time it runs.
How It Works
┌─────────────────────────────────────────────────────────┐
│ RAG Pipeline │
│ │
│ ┌──────────┐ ┌──────────────┐ ┌───────────────┐ │
│ │ User │ │ Embedding │ │ Vector │ │
│ │ Query │───►│ Model │───►│ Search │ │
│ │ │ │ (query → │ │ (find top-K │ │
│ │ │ │ vector) │ │ matches) │ │
│ └──────────┘ └──────────────┘ └───────┬───────┘ │
│ │ │
│ Retrieved chunks │
│ │ │
│ ┌──────────────────────────────────────────┐ │ │
│ │ LLM Context │ │ │
│ │ │ │ │
│ │ System: "Answer based on the provided │ │ │
│ │ context. If the answer isn't in the │ │ │
│ │ context, say so." │◄┘ │
│ │ │ │
│ │ Context: [retrieved chunks] │ │
│ │ │ │
│ │ User: [original query] │ │
│ │ │ │
│ └──────────────────┬───────────────────────┘ │
│ │ │
│ ▼ │
│ LLM Response │
│ (grounded in your actual data) │
└─────────────────────────────────────────────────────────┘The key insight: instead of teaching the model your data (fine-tuning), you show it the relevant data every time it answers a question.
Building a RAG Pipeline
Here is a production RAG implementation using the patterns we use at CODERCOPS:
// src/rag/pipeline.ts
import { Anthropic } from "@anthropic-ai/sdk";
import { ChromaClient } from "chromadb";
interface RAGConfig {
collectionName: string;
topK: number;
minRelevanceScore: number;
maxContextTokens: number;
}
class RAGPipeline {
private chroma: ChromaClient;
private anthropic: Anthropic;
private config: RAGConfig;
constructor(config: RAGConfig) {
this.chroma = new ChromaClient();
this.anthropic = new Anthropic();
this.config = config;
}
// Step 1: Ingest documents into vector store
async ingest(documents: Array<{ id: string; text: string; metadata: Record<string, unknown> }>) {
const collection = await this.chroma.getOrCreateCollection({
name: this.config.collectionName,
});
// Chunk documents (critical for quality)
const chunks: Array<{ id: string; text: string; metadata: Record<string, unknown> }> = [];
for (const doc of documents) {
const docChunks = this.chunkDocument(doc.text, {
chunkSize: 512,
chunkOverlap: 50,
});
for (let i = 0; i < docChunks.length; i++) {
chunks.push({
id: `${doc.id}-chunk-${i}`,
text: docChunks[i],
metadata: {
...doc.metadata,
sourceDocId: doc.id,
chunkIndex: i,
},
});
}
}
// Embed and store
await collection.add({
ids: chunks.map((c) => c.id),
documents: chunks.map((c) => c.text),
metadatas: chunks.map((c) => c.metadata),
});
return { chunksIngested: chunks.length };
}
// Step 2: Query with RAG
async query(userQuery: string): Promise<{
answer: string;
sources: Array<{ text: string; metadata: Record<string, unknown>; score: number }>;
tokensUsed: number;
}> {
const collection = await this.chroma.getCollection({
name: this.config.collectionName,
});
// Retrieve relevant chunks
const results = await collection.query({
queryTexts: [userQuery],
nResults: this.config.topK,
});
// Filter by relevance score
const relevantChunks: Array<{ text: string; metadata: Record<string, unknown>; score: number }> = [];
for (let i = 0; i < (results.documents?.[0]?.length || 0); i++) {
const score = 1 - (results.distances?.[0]?.[i] || 1); // Convert distance to similarity
if (score >= this.config.minRelevanceScore) {
relevantChunks.push({
text: results.documents![0]![i]!,
metadata: results.metadatas?.[0]?.[i] as Record<string, unknown> || {},
score,
});
}
}
if (relevantChunks.length === 0) {
return {
answer:
"I don't have information about that in my knowledge base. Could you rephrase your question?",
sources: [],
tokensUsed: 0,
};
}
// Build context
const context = relevantChunks
.map(
(chunk, i) =>
`[Source ${i + 1}] (relevance: ${(chunk.score * 100).toFixed(0)}%)\n${chunk.text}`
)
.join("\n\n---\n\n");
// Generate answer
const response = await this.anthropic.messages.create({
model: "claude-sonnet-4-20250514",
max_tokens: 1024,
system: `You are a helpful assistant that answers questions based on the provided context.
RULES:
- Only use information from the provided context
- If the context doesn't contain the answer, say "I don't have that information"
- Cite sources using [Source N] notation
- Be concise and direct
- Never make up information not in the context`,
messages: [
{
role: "user",
content: `Context:\n${context}\n\n---\n\nQuestion: ${userQuery}`,
},
],
});
const answer =
response.content[0].type === "text"
? response.content[0].text
: "";
return {
answer,
sources: relevantChunks,
tokensUsed: response.usage.input_tokens + response.usage.output_tokens,
};
}
// Chunking strategy (critical for RAG quality)
private chunkDocument(
text: string,
config: { chunkSize: number; chunkOverlap: number }
): string[] {
const sentences = text.match(/[^.!?]+[.!?]+/g) || [text];
const chunks: string[] = [];
let currentChunk = "";
for (const sentence of sentences) {
if (
(currentChunk + sentence).split(/\s+/).length >
config.chunkSize
) {
if (currentChunk) {
chunks.push(currentChunk.trim());
// Overlap: keep last N words
const words = currentChunk.split(/\s+/);
currentChunk =
words.slice(-config.chunkOverlap).join(" ") + " ";
}
}
currentChunk += sentence + " ";
}
if (currentChunk.trim()) {
chunks.push(currentChunk.trim());
}
return chunks;
}
}When RAG Is the Right Choice
RAG excels in these scenarios:
- Large knowledge bases. Company documentation, product catalogs, legal documents -- anything too large to fit in a prompt.
- Frequently changing data. Unlike fine-tuning (which requires retraining), RAG updates instantly when you update the index.
- Verifiability matters. RAG can cite specific sources, making it auditable.
- Multi-source knowledge. Combine information from databases, documents, APIs, and websites.
Real Example: QueryLytic
Our project QueryLytic uses RAG as its core architecture. QueryLytic lets users ask natural language questions about their data and get answers grounded in their actual database.
User: "What was our revenue growth last quarter compared to the same quarter last year?"
RAG Pipeline:
1. Query → embedding → vector search against schema documentation
2. Retrieved context: table schemas, column descriptions, relevant SQL examples
3. LLM generates SQL query grounded in actual schema
4. SQL executes against database
5. LLM explains results in natural language
Result: "Q4 2025 revenue was $2.3M, up 34% from Q4 2024's $1.7M.
Growth was driven primarily by the enterprise segment (+52%)."
[Source: revenue_quarterly table, customer_segments table]RAG is essential here because the model needs to know the exact schema, table names, and column types. Prompt engineering cannot handle the variety of possible schemas across clients. Fine-tuning would require retraining per client. RAG lets us index each client's schema and provide it at query time.
RAG Quality Factors
RAG quality depends heavily on retrieval quality. Common issues and solutions:
| Issue | Symptom | Solution |
|---|---|---|
| Chunks too large | Retrieved context has low signal-to-noise | Reduce chunk size to 256-512 tokens |
| Chunks too small | Missing context, partial information | Increase chunk size; add overlap |
| Wrong chunks retrieved | Irrelevant context confuses the model | Improve embeddings, add metadata filtering |
| Not enough chunks | Answer incomplete | Increase top-K, add re-ranking step |
| Outdated index | Stale information | Automate re-indexing on data changes |
| Lost structure | Tables/lists broken by chunking | Use structure-aware chunking |
Advanced RAG Patterns
Hybrid search: Combine vector similarity search with keyword (BM25) search. Vector search captures semantic meaning; keyword search catches exact matches.
async function hybridSearch(query: string, topK: number) {
const [vectorResults, keywordResults] = await Promise.all([
vectorStore.search(query, topK * 2),
keywordIndex.search(query, topK * 2),
]);
// Reciprocal Rank Fusion to combine results
const scores = new Map<string, number>();
const k = 60; // RRF constant
vectorResults.forEach((result, rank) => {
const score = 1 / (k + rank);
scores.set(result.id, (scores.get(result.id) || 0) + score);
});
keywordResults.forEach((result, rank) => {
const score = 1 / (k + rank);
scores.set(result.id, (scores.get(result.id) || 0) + score);
});
// Sort by combined score
return Array.from(scores.entries())
.sort((a, b) => b[1] - a[1])
.slice(0, topK);
}Query expansion: Rewrite the user's query to improve retrieval before searching.
async function expandQuery(originalQuery: string): Promise<string[]> {
const response = await llm.complete({
prompt: `Generate 3 alternative phrasings for this search query.
Include synonyms and related terms.
Query: "${originalQuery}"
Return JSON: { "alternatives": ["...", "...", "..."] }`,
});
const parsed = JSON.parse(response.content);
return [originalQuery, ...parsed.alternatives];
}Re-ranking: After initial retrieval, use a cross-encoder model to re-rank results for better precision.
Cost Profile
| Item | Cost |
|---|---|
| Implementation | 1-3 weeks engineering ($5K-$15K) |
| Vector database | $50-$500/month (Pinecone, Weaviate, Chroma) |
| Embedding costs | ~$0.10 per 1M tokens (one-time + updates) |
| Per-query cost | API costs + embedding query + retrieval |
| Maintenance | Index updates, chunk optimization |
| Total first-year cost (10K queries/day) | $1,000-$5,000/month |
Fine-Tuning: The Heavy Artillery
Fine-tuning modifies the model's internal weights using your training data. The model literally learns new patterns, behaviors, and knowledge from your examples. It is the most powerful approach but also the most expensive and complex.
How It Works
┌─────────────────────────────────────────────────┐
│ Fine-Tuning Process │
│ │
│ ┌───────────────┐ │
│ │ Training Data │ │
│ │ (500-10,000 │ │
│ │ examples) │ │
│ └───────┬───────┘ │
│ │ │
│ ▼ │
│ ┌───────────────┐ ┌────────────────────┐ │
│ │ Base Model │───►│ Training Loop │ │
│ │ (e.g., Claude │ │ - Adjust weights │ │
│ │ or GPT-4o) │ │ - Minimize loss │ │
│ └───────────────┘ │ - Validate quality │ │
│ └──────────┬───────────┘ │
│ │ │
│ ▼ │
│ ┌────────────────────┐ │
│ │ Fine-Tuned Model │ │
│ │ (new behavior │ │
│ │ baked in) │ │
│ └────────────────────┘ │
└─────────────────────────────────────────────────┘After fine-tuning, the model's default behavior has changed. You do not need to provide examples or context at inference time -- the model has internalized the patterns.
When Fine-Tuning Is Worth It
Fine-tuning makes sense in a narrow set of scenarios:
- Specialized output format. The model needs to consistently produce a very specific format that is hard to achieve with prompting alone.
- Domain-specific reasoning. The model needs to reason like a domain expert (medical, legal, financial) in ways that go beyond what few-shot examples can teach.
- Behavioral consistency at scale. You need the model to behave identically across millions of queries with zero prompt variance.
- Latency optimization. Fine-tuned models can be smaller (LoRA/QLoRA) and faster while maintaining quality for specific tasks.
- Cost optimization at volume. A fine-tuned smaller model can replace a larger model with prompt engineering, reducing per-query costs.
Fine-Tuning Example: Structured Data Extraction
# training_data.jsonl
# Each line is a training example
{"messages": [
{"role": "system", "content": "Extract structured invoice data."},
{"role": "user", "content": "Invoice #INV-2025-0392\nDate: March 15, 2025\nFrom: Acme Corp (Tax ID: 12-3456789)\nTo: Widget Inc\n\nItems:\n- Widget A x 100 @ $5.00 = $500.00\n- Widget B x 50 @ $12.00 = $600.00\n\nSubtotal: $1,100.00\nTax (8%): $88.00\nTotal: $1,188.00\nDue: April 14, 2025"},
{"role": "assistant", "content": "{\"invoice_number\": \"INV-2025-0392\", \"date\": \"2025-03-15\", \"vendor\": {\"name\": \"Acme Corp\", \"tax_id\": \"12-3456789\"}, \"customer\": {\"name\": \"Widget Inc\"}, \"items\": [{\"description\": \"Widget A\", \"quantity\": 100, \"unit_price\": 5.00, \"total\": 500.00}, {\"description\": \"Widget B\", \"quantity\": 50, \"unit_price\": 12.00, \"total\": 600.00}], \"subtotal\": 1100.00, \"tax_rate\": 0.08, \"tax_amount\": 88.00, \"total\": 1188.00, \"due_date\": \"2025-04-14\"}"}
]}
# ... hundreds more examples covering edge cases# fine_tune.py
from openai import OpenAI
client = OpenAI()
# Upload training file
file = client.files.create(
file=open("training_data.jsonl", "rb"),
purpose="fine-tune"
)
# Create fine-tuning job
job = client.fine_tuning.jobs.create(
training_file=file.id,
model="gpt-4o-mini-2024-07-18", # Base model
hyperparameters={
"n_epochs": 3,
"batch_size": 4,
"learning_rate_multiplier": 1.0,
},
suffix="invoice-extractor",
)
print(f"Fine-tuning job created: {job.id}")
# Training typically takes 30 min to several hoursFine-Tuning Quality Requirements
The quality of your training data is everything. Bad data produces a bad model.
| Factor | Requirement | Why |
|---|---|---|
| Quantity | 500-10,000 examples | Less = underfitting; more = better (diminishing returns) |
| Quality | Expert-validated, consistent | Model learns from errors in training data |
| Diversity | Cover edge cases and variations | Model generalizes from training distribution |
| Format consistency | Same structure across examples | Inconsistency confuses training |
| Balance | Roughly equal representation of categories | Avoids bias toward overrepresented cases |
Cost Profile
| Item | Cost |
|---|---|
| Data curation | 2-4 weeks ($10K-$30K or significant internal time) |
| Training (OpenAI) | $25/M tokens (training), varies by model |
| Training (self-hosted) | GPU compute: $500-$5,000 per training run |
| Implementation | 3-8 weeks engineering ($15K-$50K) |
| Per-query cost | Similar to base model (or lower for LoRA) |
| Retraining | Every time data changes significantly |
| Total first-year cost (10K queries/day) | $3,000-$15,000/month |
The Decision Framework
Here is the decision tree we use at CODERCOPS when advising clients:
START: "I need AI that works for my specific use case"
│
├── Is the customization about HOW the model responds?
│ (tone, format, length, persona)
│ │
│ └── YES → PROMPT ENGINEERING
│ Cost: $, Time: hours, Complexity: low
│
├── Is the customization about WHAT the model knows?
│ (your documents, products, policies, data)
│ │
│ └── YES → Does the data change frequently?
│ │
│ ├── YES → RAG
│ │ Cost: $$, Time: weeks, Complexity: medium
│ │
│ └── NO → Does it fit in the context window?
│ │
│ ├── YES → PROMPT ENGINEERING
│ │ (include data in prompt)
│ │
│ └── NO → RAG
│
├── Is the customization about DEEP behavioral patterns?
│ (specialized reasoning, domain expertise, consistent style)
│ │
│ └── YES → Do you have 500+ curated examples?
│ │
│ ├── YES → FINE-TUNING
│ │ Cost: $$$, Time: months, Complexity: high
│ │
│ └── NO → Start with PROMPT ENGINEERING
│ Collect examples → revisit fine-tuning later
│
└── Multiple needs? → COMBINE APPROACHES
(common: RAG + Prompt Engineering)
(advanced: Fine-Tuned model + RAG)Combining Approaches
In practice, most production systems combine two or all three approaches. Here is how they compose:
RAG + Prompt Engineering (Most Common)
Use RAG to retrieve relevant data and prompt engineering to control how the model uses that data.
const systemPrompt = `You are a product specialist for TechStore.
BEHAVIOR RULES (prompt engineering):
- Be enthusiastic but honest
- Never badmouth competitors
- Always mention our price match guarantee
- Keep responses under 100 words
RETRIEVED PRODUCT DATA (RAG):
{retrievedContext}
Answer the customer's question using only the product data above.`;This is what we use in 70% of our AI projects. RAG provides the knowledge; prompt engineering provides the behavior.
Fine-Tuned Model + RAG (Advanced)
Fine-tune a model for domain-specific reasoning, then use RAG to provide current data.
Example: A medical triage system where the model is fine-tuned on clinical reasoning patterns (how to ask follow-up questions, when to escalate) and RAG provides the latest clinical guidelines and drug interaction databases.
All Three
Fine-tune for behavioral patterns, RAG for dynamic knowledge, prompt engineering for session-level customization.
This is rare and expensive. We have only built one system that truly needed all three (a financial advisory tool that needed domain reasoning + current market data + per-client customization).
Performance Benchmarks: Side-by-Side
We ran a controlled benchmark on a customer support task using the same underlying model and data.
Setup
- Task: Answer customer questions about a SaaS product with 200 help articles.
- Evaluation: 100 test questions, graded by LLM judge + human review.
- Model: Claude Sonnet 4.
| Metric | Prompt Only | RAG | Fine-Tuned | RAG + Fine-Tuned |
|---|---|---|---|---|
| Accuracy | 62% | 89% | 78% | 93% |
| Hallucination rate | 23% | 4% | 12% | 2% |
| Avg response time | 1.2s | 2.1s | 1.0s | 1.8s |
| Cost per query | $0.003 | $0.008 | $0.002 | $0.007 |
| Source citations | No | Yes | No | Yes |
| Handles new articles | No | Instantly | After retrain | Instantly |
Key takeaways:
- RAG dramatically reduces hallucination (from 23% to 4%) by grounding responses in actual data.
- Fine-tuning reduces cost (smaller model, fewer tokens) but does not eliminate hallucination as effectively.
- The combination beats both individual approaches on accuracy (93% vs 89% and 78%).
- RAG adds latency (~0.9s for retrieval) but provides verifiable source citations.
Implementation Timelines
| Phase | Prompt Engineering | RAG | Fine-Tuning |
|---|---|---|---|
| Research and design | 1-2 days | 3-5 days | 1-2 weeks |
| Data preparation | Write examples (hours) | Index documents (days) | Curate dataset (weeks) |
| Implementation | 1-3 days | 1-2 weeks | 2-4 weeks |
| Testing and iteration | 1-2 days | 3-5 days | 1-2 weeks |
| Production deployment | Same day | 1-2 days | 1 week |
| Total | 3-7 days | 2-4 weeks | 6-10 weeks |
When to Start and When to Upgrade
We recommend a progressive approach:
Week 1: Start with Prompt Engineering
└── Establish baseline performance
└── Identify gaps
Week 2-3: Add RAG if needed
└── For knowledge-dependent features
└── Compare against prompt-only baseline
Month 2+: Consider Fine-Tuning if needed
└── Only if prompt + RAG ceiling is too low
└── Collect training data from production usage
└── A/B test against existing approachMost projects never need to reach the fine-tuning stage. Prompt engineering + RAG covers 85-90% of use cases in our experience.
Cost Comparison Over 12 Months
For a system handling 10,000 queries per day:
| Cost Category | Prompt Engineering | RAG | Fine-Tuning | RAG + PE |
|---|---|---|---|---|
| Setup cost | $2K | $10K | $40K | $12K |
| Monthly API | $900 | $1,200 | $600 | $1,200 |
| Monthly infra | $0 | $200 | $500 | $200 |
| Monthly maintenance | $500 | $1,000 | $2,000 | $1,200 |
| Annual total | $19K | $39K | $77K | $43K |
Prompt engineering is roughly half the cost of RAG and a quarter the cost of fine-tuning. But the quality ceiling is also lower. The right answer depends on what quality level your use case demands.
The CODERCOPS Recommendation
Here is our standard advice to clients:
Start with prompt engineering. Always. It costs almost nothing and establishes a baseline. You might be surprised how far good prompts can take you.
Add RAG when you hit the knowledge wall. If the model needs to know things it does not know (your data, your documents, your products), RAG is the answer. Not fine-tuning. RAG.
Reserve fine-tuning for specialized behavior. If you need the model to reason like a domain expert, consistently produce a very specific output format, or you are optimizing for cost at massive scale, fine-tuning may be justified. But get there incrementally, not as a first step.
Always combine with prompt engineering. Whether you are using RAG, fine-tuning, or both, prompt engineering is the control layer. It shapes behavior, sets guardrails, and handles the session-level customization that the other approaches cannot.
At CODERCOPS, our AI integration services include strategy consulting on exactly this decision. We have built systems using all three approaches and every combination. The right answer is always project-specific, but the framework above will get you 90% of the way to the right decision.
Need help choosing and implementing the right AI strategy for your product? CODERCOPS builds production AI systems using RAG, fine-tuning, and advanced prompt engineering. Talk to us about your use case.
Comments