Retrieval-Augmented Generation (RAG) promises to solve the hallucination problem by grounding large language models in factual, up-to-date information. The demo works beautifully: upload some documents, embed them in a vector database, retrieve relevant chunks, and generate answers. But between the demo and production lies a chasm of complexity that most RAG tutorials conveniently ignore.
Building reliable RAG systems isn't just about choosing the right embedding model or vector database—it's about creating robust pipelines that handle real-world data messiness, maintain consistency under load, and provide observable, debuggable behavior when things go wrong.
Document Processing is Critical: Invest heavily in robust document processing and chunking strategies
Hybrid Retrieval: Combine dense and sparse retrieval for better coverage and accuracy
Quality Control: Implement comprehensive validation and citation tracking
Evaluation Framework: Build automated testing and continuous evaluation pipelines
Production Readiness: Design for scale, observability, and reliability from day one
Context Matters: Preserve semantic context across chunks and conversations
Iterative Improvement: Use evaluation metrics to continuously optimize your pipeline
Building reliable RAG systems is engineering-intensive work that requires careful attention to data quality, system architecture, and evaluation methodology. The payoff is AI systems that provide accurate, attributable, and trustworthy information—exactly what enterprises need for mission-critical applications.
Need help building production-ready RAG systems? Discuss your requirements with our AI engineering team.
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class SemanticChunker:
def __init__(self, embedder, similarity_threshold=0.8):
self.embedder = embedder
self.similarity_threshold = similarity_threshold
def chunk(self, text: str) -> List[Chunk]:
sentences = self.split_sentences(text)
embeddings = self.embedder.embed_batch(sentences)
chunks = []
current_chunk = [sentences[0]]
current_embedding = embeddings[0]
for i in range(1, len(sentences)):
similarity = cosine_similarity(current_embedding, embeddings[i])
if similarity < self.similarity_threshold:
# Start new chunk
chunks.append(Chunk(
content=' '.join(current_chunk),
embedding=current_embedding
))
current_chunk = [sentences[i]]
current_embedding = embeddings[i]
else:
# Add to current chunk
current_chunk.append(sentences[i])
current_embedding = self.update_embedding(
current_embedding,
embeddings[i],
len(current_chunk)
)
return chunks
class HierarchicalChunker:
def chunk(self, document: Document) -> ChunkHierarchy:
# Create multi-level chunks
sections = self.extract_sections(document)
paragraphs = [p for section in sections for p in section.paragraphs]
sentences = [s for paragraph in paragraphs for s in paragraph.sentences]
return ChunkHierarchy(
document_level=DocumentChunk(document.content),
section_level=[SectionChunk(s.content) for s in sections],
paragraph_level=[ParaChunk(p.content) for p in paragraphs],
sentence_level=[SentChunk(s.content) for s in sentences]
)
class ContextPreservingChunker:
def __init__(self, overlap_size=100):
self.overlap_size = overlap_size
def chunk(self, text: str) -> List[ContextualChunk]:
base_chunks = self.base_chunking(text)
contextual_chunks = []
for i, chunk in enumerate(base_chunks):
# Add preceding context
prev_context = ""
if i > 0:
prev_context = base_chunks[i-1].content[-self.overlap_size:]
# Add following context
next_context = ""
if i < len(base_chunks) - 1:
next_context = base_chunks[i+1].content[:self.overlap_size]
contextual_chunks.append(ContextualChunk(
content=chunk.content,
prev_context=prev_context,
next_context=next_context,
position=i
))
return contextual_chunks
