Improving outputs from large language models is rarely a question of "which single tool" to use. It is a design choice that balances accuracy, latency, cost, maintenance, and safety.
This article provides a thorough, practical comparison of the three dominant approaches: prompt engineering, RAG, and fine-tuning. So you can choose and combine them effectively for real products.
TL;DR for each approach
Prompt engineering: Change the input (the prompt) to better activate the model's existing knowledge and skills.
RAG: Give the model fresh, domain-specific evidence by retrieving external content and appending it to the prompt.
Fine-tuning: Change the model itself by training it on domain examples so the knowledge and behavior are baked into its weights.
Prompt engineering: what's it all about?
Prompt engineering shapes how the model interprets and prioritizes information that already exists in its parameters.
The challenge with prompt engineering is consistency. Prompts evolve over time, and subtle changes can produce significantly different results. This is where Langbase Pipes become useful.
- You can experiment with variations side by side.
- You can roll back to earlier versions.
- You can track which prompts deliver consistent, high-quality results.
This approach allows you to iterate rapidly without losing control, much like developers use Git for code.
By the way, Langbase provides memory agents, the most inexpensive RAG solution.
Key techniques
Role and instruction framing (system or lead-in statement that defines tone, role, and constraints).
Few-shot prompting (showing examples to demonstrate desired style/logic).
Chain-of-thought or step-by-step prompts to improve reasoning.
Output constraints (formatting, JSON schema, length, explicit style rules).
Prompt templates and variable substitution for repeatable tasks.
When to use it
Fast iteration, prototyping, and low-cost improvements.
When you cannot or do not want to change model weights or add infra.
To enforce formatting and to reduce simple ambiguity in user input.
Limitations and risks
Cannot add factual knowledge that the model does not already have.
Brittleness: minor wording changes can produce different results.
Can't solve problems that require current data beyond the model's cutoff.
Evaluation is often empirical and requires careful A/B testing and versioning of prompts.
Best practices
Maintain prompt templates under version control and track experiments.
Use unit tests and automated checks for format and safety.
Combine with lightweight verification (e.g., regex checks, parsers) to catch format violations.
RAG: how it works and doesn't
RAG augments model output with material retrieved from a document corpus (internal knowledge base, web, PDFs, etc.). Technically this is "retrieve, augment, generate."
With Langbase Memory, you can implement RAG directly. Memory lets you ingest documents, store embeddings, and retrieve relevant chunks during a conversation.
High-level architecture
Ingest: documents are preprocessed, split into chunks, and embedded.
Store: embeddings and chunk metadata are stored in a vector database.
Retrieve: for each user query, compute an embedding and fetch top-k semantically similar chunks.
Re-rank and filter: optionally re-score retrieved results with a secondary model or heuristics.
Augment prompt: concatenate the selected chunks or their summaries with the original query.
Generate: the LLM produces an answer conditioned on the augmented prompt.
Langbase Memory (RAG) architecture
Why RAG is valuable
Gives access to up-to-date, domain-specific facts without retraining the model.
Enables provenance: you can link answers back to documents or passages.
Good for domains where the base model's cutoff or coverage is insufficient.
Operational trade-offs
Latency: retrieving and re-ranking adds time per query.
Infrastructure: requires embedding services, a vector DB, and periodic re-ingestion.
Cost: embedding + storage + retrieval + LLM calls can be materially more expensive.
Hallucination risk: the model can still hallucinate or over-generalize even with retrieved context; requiring explicit citation and grounding helps
Best practices
Chunk documents with overlap to preserve context but avoid redundancy.
Precompute and refresh embeddings when sources change.
Use re-rankers (BM25, cross-encoders) to improve precision.
Limit context length and prioritize high-quality sources.
Surface provenance (document id, snippet, URL) with each claim.
Fine-tuning
Fine-tuning updates model weights by training on a labeled, domain-specific dataset. Variants include full fine-tuning and parameter-efficient methods (LoRA, adapters, PEFT), which change fewer parameters for lower cost.
What fine-tuning achieves
Embeds domain knowledge and preferred behaviors directly into the model.
Improves consistency for specialized tasks and can reduce the need for long context windows at inference.
Eliminates the per-query retrieval overhead if all needed knowledge can be encoded in the model.
Requirements and costs
Data: high quality, well-labeled examples are essential. Thousands of curated examples are typical for nontrivial tasks.
Compute: training requires GPUs or managed training services; costs can escalate for large models
Maintenance: to update knowledge you need to retrain or adapt the model; versioning and rollback mechanisms are necessary.
Risks: catastrophic forgetting (losing general capabilities), overfitting to training data, possible introduction of biases.
When to choose fine-tuning
When you need very high performance on a narrow, stable domain.
When latency must be minimal and predictable.
When privacy/regulatory constraints require on-device or on-premise models with no external retrieval.
Best practices
Hold out evaluation and test sets that reflect production prompts.
Use parameter-efficient methods where possible to reduce compute and avoid full retrains.
Monitor general-purpose performance post-tuning to detect catastrophic forgetting.
Keep fine-tuned models versioned and provide an easy rollback path
Comparing the approaches (concise)
Prompt engineering improves clarity and control without infrastructure changes but cannot expand a model's knowledge.
RAG provides fresh, domain-specific evidence at the cost of extra infrastructure, latency, and complexity.
Fine-tuning embeds deep expertise into the model itself, delivering faster inference and specialized behavior, but requires data, compute, and maintenance.
Most production systems use a hybrid: fine-tune where stable expertise is needed, use RAG to add recent or large external corpora, and apply prompt engineering to shape output and enforce constraints.
Example: Legal AI agent (detailed pipeline)
Ingest firm knowledge: policies, playbooks, annotated past briefs → chunk, embed, store in a secure vector DB.
Fine-tune a core model on firm templates and permitted language to internalize style, disclaimers, and firm policy.
At query time:
- Compute query embedding; retrieve top-k passages from vector DB.
- Re-rank passages via a cross-encoder or BM25 hybrid.
- Construct a controlled prompt that includes: the most relevant passages, an instruction to cite sources, and a JSON output schema.
- Run generation on the fine-tuned model.
- Run a verifier that checks claims against retrieved passages; if inconsistencies appear, flag for human review.
- Return response with inline citations and an "evidence" panel for the user to inspect.
This hybrid approach gives fast, policy-compliant writing, up-to-date legal citations, and auditability through provenance.
Decision flow: which to pick first
Want immediate, low-cost improvements? Start with prompt engineering.
Need current facts or large corpora accessible at query time? Implement RAG.
Need high, repeatable accuracy in a narrow domain and can afford training? Fine-tune (or use parameter-efficient tuning).
Complex, production use cases often require all three: fine-tuning for domain rules, RAG for fresh evidence, and prompt engineering for consistent outputs and safety controls.
At Langbase, we build, deploy and scale AI agents, co-powered by these approaches.
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